[ { "path": "README.md", "content": "# YarnGPT 🎙️\n![image/png](https://github.com/saheedniyi02/yarngpt/blob/main/notebooks%2Faudio_0c026c21-f432-4d20-a86b-899a10d9ed60.webp)\nA text-to-speech model generating natural Nigerian-accented English speech. Built on pure language modeling without external adapters.\n\nWeb Url: https://yarngpt.co/\n\n## Quick Start\n\n```python\n\n!git clone https://github.com/saheedniyi02/yarngpt.git\n\npip install outetts uroman\n\nimport os\nimport re\nimport json\nimport torch\nimport inflect\nimport random\nimport uroman as ur\nimport numpy as np\nimport torchaudio\nimport IPython\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom outetts.wav_tokenizer.decoder import WavTokenizer\n\n\n!wget https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\n!gdown 1-ASeEkrn4HY49yZWHTASgfGFNXdVnLTt\n\n\nfrom yarngpt.audiotokenizer import AudioTokenizerV2\n\ntokenizer_path=\"saheedniyi/YarnGPT2\"\nwav_tokenizer_config_path=\"/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\"\nwav_tokenizer_model_path = \"/content/wavtokenizer_large_speech_320_24k.ckpt\"\n\n\naudio_tokenizer=AudioTokenizerV2(\n tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path\n )\n\n\nmodel = AutoModelForCausalLM.from_pretrained(tokenizer_path,torch_dtype=\"auto\").to(audio_tokenizer.device)\n\n#change the text\ntext=\"The election was won by businessman and politician, Moshood Abiola, but Babangida annulled the results, citing concerns over national security.\"\n\n# change the language and voice\nprompt=audio_tokenizer.create_prompt(text,lang=\"english\",speaker_name=\"idera\")\n\ninput_ids=audio_tokenizer.tokenize_prompt(prompt)\n\noutput = model.generate(\n input_ids=input_ids,\n temperature=0.1,\n repetition_penalty=1.1,\n max_length=4000,\n #num_beams=5,# using a beam size helps for the local languages but not english\n )\n\ncodes=audio_tokenizer.get_codes(output)\naudio=audio_tokenizer.get_audio(codes)\nIPython.display.Audio(audio,rate=24000)\ntorchaudio.save(f\"Sample.wav\", audio, sample_rate=24000)\n\n```\n\n## Features\n\n- 🗣️ 12 preset voices (6 male, 6 female)\n- 🎯 Trained on 2000+ hours of Nigerian audio\n- 🔊 24kHz high-quality audio output\n- 🚀 Simple API for quick integration\n- 📝 Support for long-form text\n\n## Available Voices\n- Female: zainab, idera, regina, chinenye, joke, remi\n- Male: jude, tayo, umar, osagie, onye, emma\n\n## Examples\n\nCheck out our [demo notebook](link-to-notebook) or listen to [sample outputs](https://huggingface.co/saheedniyi/YarnGPT/tree/main/audio).\n\n## Model Details\n\n- Base: [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M)\n- Training: 5 epochs on A100 GPU\n- Data: Nigerian movies, podcasts, and open-source audio\n- Architecture: Pure language modeling approach\n\n## Limitations\n\n- English to Nigerian-accented English only\n- May not capture all Nigerian accent variations\n- Training data includes auto-generated content\n\n## Citation\n\n```bibtex\n@misc{yarngpt2025,\n author = {Saheed Azeez},\n title = {YarnGPT: Nigerian-Accented English Text-to-Speech Model},\n year = {2025},\n publisher = {Hugging Face}\n}\n```\n\n## License\nMIT\n\n## Acknowledgments\nBuilt with [WavTokenizer](https://github.com/jishengpeng/WavTokenizer) and inspired by [OuteTTS](https://huggingface.co/OuteAI/OuteTTS-0.2-500M/).\n" }, { "path": "__init__.py", "content": "\n" }, { "path": "audiotokenizer.py", "content": "import os\nimport re\nimport json\nimport torch\nimport inflect\nimport random\nimport uroman as ur\nimport numpy as np\nimport torchaudio\nfrom transformers import AutoTokenizer\nfrom outetts.wav_tokenizer.decoder import WavTokenizer\nfrom outetts.wav_tokenizer.encoder.utils import convert_audio\n\nclass AudioTokenizer:\n\n def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{audio_start}\\n\"\n self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n self.bos = \"<|im_start|>\"\n self.eos = \"<|im_end|>\"\n self.input_length=0\n self.special_tokens = {\n \"audio_code\": \"<|{}|>\",\n \"text_start\": \"<|text_start|>\",\n \"text_end\": \"<|text_end|>\",\n \"audio_start\": \"<|audio_start|>\",\n \"audio_end\": \"<|audio_end|>\",\n \"time\": \"<|t_{:.2f}|>\",\n \"code_start\": \"<|code_start|>\",\n \"code_end\": \"<|code_end|>\",\n \"text_sep\": \"<|text_sep|>\"\n }\n self.lec = inflect.engine()\n #self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{audio_start}\\n\"\n #self.config_path = \"/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\"\n #self.model_path = \"/content/wavtokenizer_large_speech_320_24k.ckpt\"\n self.wavtokenizer = WavTokenizer.from_pretrained0802(wav_tokenizer_config_path, wav_tokenizer_model_path)\n self.wavtokenizer = self.wavtokenizer.to(self.device)\n self.BASE_DIR = os.path.dirname(__file__)\n self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, \"default_speakers\")\n self.speakers=[\"idera\",\"emma\",\"onye\",\"jude\",\"osagie\",\"tayo\",\"zainab\",\"joke\",\"regina\",\"remi\",\"umar\",\"chinenye\"]\n\n def get_speaker_path(self,speaker_name):\n return os.path.join(self.DEFAULT_SPEAKERS_DIR, f\"{speaker_name}.json\")\n\n def load_speaker(self, path: str):\n with open(path, \"r\") as f:\n return json.load(f)\n\n def load_default_speaker(self, name: str):\n name = name.lower().strip()\n speaker_path=self.get_speaker_path(name)\n return self.load_speaker(speaker_path)\n\n\n def process_text(self, text: str):\n\n text = re.sub(r'\\d+(\\.\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\n text = re.sub(r'[-_/,\\.\\\\]', ' ', text)\n text = re.sub(r'[^a-z\\s]', '', text)\n text = re.sub(r'\\s+', ' ', text).strip()\n return text.split()\n\n def create_audio_prompt(self,words: list) -> str:\n prompt = []\n for i in words:\n word = i[\"word\"]\n duration = self.special_tokens[\"time\"].format(float(i[\"duration\"]))\n tokens = \"\".join([self.special_tokens[\"audio_code\"].format(c) for c in i[\"codes\"]])\n prompt.append(f'{word}{duration}{self.special_tokens[\"code_start\"]}{tokens}{self.special_tokens[\"code_end\"]}')\n return \"\\n\".join(prompt)\n\n def create_prompt(self,text,speaker_name=\"idera\"):\n speaker=self.load_default_speaker(speaker_name)\n input_words = self.process_text(speaker[\"text\"]) + self.process_text(text)\n #input_words = process_text(speaker[\"text\"]) + input_words\n\n inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n prompt = self.text_prompt.format(\n bos=self.bos,\n text_start=self.special_tokens['text_start'],\n words=inputs_words_strings,\n text_end=self.special_tokens['text_end'],\n audio_start=self.special_tokens['audio_start']\n )\n prompt += self.create_audio_prompt(speaker[\"words\"])\n\n return prompt\n\n def tokenize_prompt(self, prompt):\n input_ids = self.tokenizer.encode(\n prompt,\n add_special_tokens=False,\n return_tensors=\"pt\"\n ).to(self.device)\n self.input_length=input_ids.shape[1]\n return input_ids.to(self.device)\n\n\n def get_audio(self,discrete_code):\n discrete_code=torch.tensor([[discrete_code]]).to(self.device)\n features = self.wavtokenizer.codes_to_features(discrete_code).to(self.device)\n bandwidth_id = torch.tensor([0]).to(self.device)\n audio_out = self.wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n return audio_out.to(\"cpu\")\n\n def extract_integers(self,s):\n # Match integers enclosed in vertical bars |integer|\n matches = re.findall(r'\\|(-?\\d+)\\|', s)\n # Convert matches to integers\n return [int(match) for match in matches]\n\n def get_codes(self, output):\n new_output=self.tokenizer.decode(output[0][self.input_length:])\n codes=self.extract_integers(new_output)\n return codes\n\n\nclass AudioTokenizerForLocal(AudioTokenizer):\n\n def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):\n super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)\n self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{lang}\\n{audio_start}\\n\"\n self.special_tokens = {\n \"audio_code\": \"<|{}|>\",\n \"text_start\": \"<|text_start|>\",\n \"text_end\": \"<|text_end|>\",\n \"audio_start\": \"<|audio_start|>\",\n \"audio_end\": \"<|audio_end|>\",\n \"word_start\": \"<|word_start|>\",\n \"word_end\": \"<|word_end|>\",\n \"time\": \"<|t_{:.2f}|>\",\n \"code_start\": \"<|code_start|>\",\n \"code_end\": \"<|code_end|>\",\n \"text_sep\": \"<|text_sep|>\",\n \"hausa\":\"<|hausa|>\",\n \"igbo\":\"<|igbo|>\",\n \"yoruba\":\"<|yoruba|>\",\n }\n self.uroman = ur.Uroman()\n self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, \"default_speakers_local\")\n self.speakers = [\n \"hausa_male1\", \"hausa_male2\",\"yoruba_male1\", \"yoruba_male2\",\"igbo_male2\" #\"igbo_male1\", \"igbo_male2\",\n \"hausa_female1\", \"hausa_female2\", \"igbo_female1\", \"igbo_female2\", \"yoruba_female1\", \"yoruba_female2\"\n ]\n \n def process_text(self, text: str):\n text = self.uroman.romanize_string(text)\n text = re.sub(r'\\d+(\\.\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\n text = re.sub(r'[-_/,\\.\\\\]', ' ', text)\n text = re.sub(r'[^a-z\\s]', '', text)\n text = re.sub(r'\\s+', ' ', text).strip()\n return text.split()\n\n def create_prompt(self,text,lang,speaker_name=None):\n assert lang in [\"hausa\",\"igbo\",\"yoruba\"], f\"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba']\"\n #if no speaker\n if speaker_name is None:\n if lang==\"hausa\":\n speaker_name=random.choice([\"hausa_male1\",\"hausa_male2\",\"hausa_female1\",\"hausa_female2\"])\n elif lang==\"igbo\":\n speaker_name=random.choice([\"igbo_female1\",\"igbo_female2\",\"igbo_male2\"])#\"igbo_male1\"])\n else:\n speaker_name=random.choice([\"yoruba_male2\",\"yoruba_female1\",\"yoruba_female2\"])\n speaker=self.load_default_speaker(speaker_name)\n input_words = self.process_text(speaker[\"text\"]) + self.process_text(text)\n #input_words = process_text(speaker[\"text\"]) + input_words\n\n inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n prompt = self.text_prompt.format(\n bos=self.bos,\n text_start=self.special_tokens['text_start'],\n words=inputs_words_strings,\n text_end=self.special_tokens['text_end'],\n lang=self.special_tokens[lang],\n audio_start=self.special_tokens['audio_start']\n )\n prompt += self.create_audio_prompt(speaker[\"words\"])\n\n return prompt\n\n\nclass AudioTokenizerV2(AudioTokenizer):\n\n def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):\n super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)\n self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{lang}\\n{audio_start}\\n\"\n self.asr_prompt=\"{bos}\\n{code_start}{codes}{code_end}\\n{asr}\\n\"\n self.special_tokens = {\n \"audio_code\": \"<|{}|>\",\n \"text_start\": \"<|text_start|>\",\n \"text_end\": \"<|text_end|>\",\n \"audio_start\": \"<|audio_start|>\",\n \"audio_end\": \"<|audio_end|>\",\n \"word_start\": \"<|word_start|>\",\n \"word_end\": \"<|word_end|>\",\n \"time\": \"<|t_{:.2f}|>\",\n \"code_start\": \"<|code_start|>\",\n \"code_end\": \"<|code_end|>\",\n \"text_sep\": \"<|text_sep|>\",\n \"hausa\":\"<|hausa|>\",\n \"igbo\":\"<|igbo|>\",\n \"yoruba\":\"<|yoruba|>\",\n \"english\":\"<|english|>\",#<|english|>\n \"asr\":\"<|asr|>\"\n }\n self.uroman = ur.Uroman()\n self.DEFAULT_SPEAKERS_DIR_LOCAL = os.path.join(self.BASE_DIR, \"default_speakers_local\")\n self.DEFAULT_SPEAKERS_ENG = os.path.join(self.BASE_DIR, \"default_speakers\")\n self.speakers_local = [\n \"hausa_male1\", \"hausa_male2\",\"yoruba_male1\", \"yoruba_male2\",\"igbo_male2\" #\"igbo_male1\", \"igbo_male2\",\n \"hausa_female1\", \"hausa_female2\", \"igbo_female1\", \"igbo_female2\", \"yoruba_female1\", \"yoruba_female2\"\n ]\n self.speakers_eng = [\"idera\",\"emma\",\"onye\",\"jude\",\"osagie\",\"tayo\",\"zainab\",\"joke\",\"regina\",\"remi\",\"umar\",\"chinenye\",\"saheed\"]\n self.changed_tokens=[('<|1836|>', '<|453|><|453|>'),\n ('<|1837|>', '<|1836|><|1836|>'),\n ('<|1838|>', '<|1837|><|1837|>'),\n ('<|1840|>', '<|244|><|167|>'),\n ('<|1841|>', '<|235|><|219|>'),\n ('<|1844|>', '<|453|><|244|>'),\n ('<|1845|>', '<|1838|><|1838|>')]\n\n def process_text(self, text: str):\n text = self.uroman.romanize_string(text)\n text = re.sub(r'\\d+(\\.\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\n text = re.sub(r'[-_/,\\.\\\\]', ' ', text)\n text = re.sub(r'[^a-z\\s]', '', text)\n text = re.sub(r'\\s+', ' ', text).strip()\n return text.split()\n\n def get_speaker_path(self,speaker_name,dir):\n return os.path.join(dir, f\"{speaker_name}.json\")\n\n def load_speaker(self, path: str):\n with open(path, \"r\") as f:\n return json.load(f)\n\n def load_default_speaker(self, name: str,dir: str):\n name = name.lower().strip()\n speaker_path=self.get_speaker_path(name,dir)\n return self.load_speaker(speaker_path)\n\n def create_prompt(self,text,lang,speaker_name=None):\n assert lang in [\"hausa\",\"igbo\",\"yoruba\",\"english\"], f\"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba','english']\"\n #if no speaker\n dir=self.DEFAULT_SPEAKERS_DIR_LOCAL\n if speaker_name is None:\n if lang==\"hausa\":\n speaker_name=random.choice([\"hausa_male1\",\"hausa_male2\",\"hausa_female1\",\"hausa_female2\"])\n elif lang==\"igbo\":\n speaker_name=random.choice([\"igbo_female1\",\"igbo_female2\",\"igbo_male2\"])#\"igbo_male1\"])\n elif lang==\"yoruba\":\n speaker_name=random.choice([\"yoruba_male2\",\"yoruba_female1\",\"yoruba_female2\"])\n else:\n speaker_name=random.choice(self.speakers_eng)\n \n if lang==\"english\":\n dir=self.DEFAULT_SPEAKERS_ENG\n speaker=self.load_default_speaker(speaker_name,dir)\n input_words = self.process_text(speaker[\"text\"]) + self.process_text(text)\n #input_words = process_text(speaker[\"text\"]) + input_words\n\n inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n prompt = self.text_prompt.format(\n bos=self.bos,\n text_start=self.special_tokens['text_start'],\n words=inputs_words_strings,\n text_end=self.special_tokens['text_end'],\n lang=self.special_tokens[lang],\n audio_start=self.special_tokens['audio_start']\n )\n prompt += self.create_audio_prompt(speaker[\"words\"])\n\n return prompt\n def replace_tokens(text):\n for pair in self.changed_tokens:\n text=text.replace(pair[0],pair[-1])\n return text \n\n def resample(self,audio: np.ndarray, sr: int, target_sr: int):\n audio = audio.to(dtype=torch.float32)\n #.clone().detach()\n audio = audio.unsqueeze(0)\n # 1 as last arg corresponds to mono audio\n resampled = convert_audio(audio, sr, target_sr, 1)\n return resampled.to(self.device )\n\n def quantize_wavtokenizer(self, path):\n audio_data, sample_rate = torchaudio.load(path)\n audio_data=audio_data.squeeze()\n audio = self.resample(audio_data, sample_rate, 24000).to(self.device)\n if audio.ndim==3:\n audio=audio.squeeze(1)\n bandwidth_id = torch.tensor([0]).to(self.device )\n _, codes = self.wavtokenizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n codes = codes.squeeze(1).to(self.device)#+last_text_token\n res=\"\"\n for code in codes[0].tolist():\n res+=f\"<|{code}|>\"\n return res\n \n def create_asr_prompt(self,audio_path):\n codes=self.quantize_wavtokenizer(audio_path)\n prompt = self.asr_prompt.format(\n bos=self.bos,\n code_start=self.special_tokens['code_start'],\n codes=codes,\n code_end=self.special_tokens['code_end'],\n asr=self.special_tokens[\"asr\"],\n )\n return prompt\n\n def get_asr_results(self,output):\n res=\"\"\n for text in self.tokenizer.decode(output[0]).split(\"<|text_start|>\")[-1].split(\"<|text_end|>\")[0].split(\"\\n\"):\n res+=text.split(\"<|word_start|>\")[-1].split(\"<|word_end|>\")[0]\n res+=\" \"\n return res.strip()\n" }, { "path": "default_speakers/azeez.json", "content": "{\n \"text\": \"Hello! My name is Saheed azeez and I am testing the audio feature\",\n \"words\": [\n {\n \"word\": \"hello\",\n \"duration\": 1.22,\n \"codes\": [\n 219,\n 244,\n 244,\n 167,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 244,\n 219,\n 237,\n 864,\n 1041,\n 1048,\n 1372,\n 1780,\n 1554,\n 1024,\n 702,\n 1814,\n 1754,\n 1315,\n 1697,\n 1719,\n 1682,\n 307,\n 621,\n 901,\n 355,\n 783,\n 1726,\n 353,\n 1416,\n 729,\n 803,\n 1494,\n 353,\n 876,\n 1818,\n 932,\n 1068,\n 1813,\n 875,\n 1774,\n 766,\n 1453,\n 1466,\n 792,\n 1388,\n 1495,\n 1236,\n 1462,\n 431,\n 1025,\n 1429,\n 1128,\n 1236,\n 1483,\n 1305,\n 1352,\n 1681,\n 5,\n 1758,\n 1481,\n 1339\n ]\n },\n {\n \"word\": \"my\",\n \"duration\": 0.18,\n \"codes\": [\n 1333,\n 1339,\n 1388,\n 1373,\n 974,\n 723,\n 1776,\n 1001,\n 1160,\n 1769,\n 1048,\n 1646,\n 1321,\n 912\n ]\n },\n {\n \"word\": \"name\",\n \"duration\": 0.2,\n \"codes\": [\n 1596,\n 325,\n 876,\n 1303,\n 973,\n 1707,\n 1332,\n 1300,\n 145,\n 1136,\n 1266,\n 1353,\n 845,\n 913,\n 989\n ]\n },\n {\n \"word\": \"is\",\n \"duration\": 0.12,\n \"codes\": [\n 1257,\n 1372,\n 1617,\n 1800,\n 1568,\n 1679,\n 1798,\n 1476,\n 1759\n ]\n },\n {\n \"word\": \"saheed\",\n \"duration\": 0.5,\n \"codes\": [\n 1807,\n 1354,\n 1737,\n 1738,\n 1060,\n 1122,\n 1195,\n 1275,\n 1129,\n 1473,\n 688,\n 1675,\n 1724,\n 1392,\n 1146,\n 1605,\n 1784,\n 1476,\n 1454,\n 1743,\n 1824,\n 706,\n 1706,\n 669,\n 91,\n 1079,\n 1456,\n 1645,\n 1041,\n 1687,\n 1425,\n 1205,\n 830,\n 1525,\n 1007,\n 1291,\n 723\n ]\n },\n {\n \"word\": \"azeez\",\n \"duration\": 0.48,\n \"codes\": [\n 829,\n 926,\n 1438,\n 1124,\n 1282,\n 1745,\n 1019,\n 1430,\n 1657,\n 1715,\n 1637,\n 1653,\n 1713,\n 1370,\n 1534,\n 1410,\n 1767,\n 814,\n 22,\n 1703,\n 1534,\n 1797,\n 1488,\n 1812,\n 1637,\n 1791,\n 1720,\n 1677,\n 1807,\n 1459,\n 1779,\n 1767,\n 1145,\n 1239,\n 1622,\n 1264\n ]\n },\n {\n \"word\": \"and\",\n \"duration\": 0.24,\n \"codes\": [\n 1780,\n 1291,\n 1174,\n 1435,\n 1494,\n 1807,\n 662,\n 1760,\n 1694,\n 363,\n 1225,\n 1775,\n 1264,\n 1455,\n 1014,\n 1758,\n 1620,\n 1013\n ]\n },\n {\n \"word\": \"i\",\n \"duration\": 0.06,\n \"codes\": [\n 1823,\n 1295,\n 1397,\n 1108,\n 1275\n ]\n },\n {\n \"word\": \"am\",\n \"duration\": 0.14,\n \"codes\": [\n 1129,\n 1697,\n 835,\n 1589,\n 1719,\n 1534,\n 1495,\n 1025,\n 1405,\n 766\n ]\n },\n {\n \"word\": \"testing\",\n \"duration\": 0.42,\n \"codes\": [\n 196,\n 1118,\n 761,\n 1314,\n 1770,\n 1138,\n 1429,\n 728,\n 1497,\n 1792,\n 1049,\n 1430,\n 1062,\n 1788,\n 1354,\n 1555,\n 1735,\n 1728,\n 954,\n 1754,\n 343,\n 1418,\n 636,\n 1501,\n 1301,\n 901,\n 763,\n 1620,\n 1687,\n 177,\n 1706,\n 325\n ]\n },\n {\n \"word\": \"the\",\n \"duration\": 0.14,\n \"codes\": [\n 810,\n 1421,\n 1404,\n 1093,\n 781,\n 752,\n 1780,\n 1749,\n 850,\n 1435\n ]\n },\n {\n \"word\": \"audio\",\n \"duration\": 0.3,\n \"codes\": [\n 1792,\n 1381,\n 1309,\n 1472,\n 1449,\n 1785,\n 114,\n 601,\n 866,\n 1764,\n 1212,\n 1453,\n 1152,\n 1777,\n 853,\n 1735,\n 1052,\n 355,\n 1421,\n 1605,\n 1761,\n 1664,\n 540\n ]\n },\n {\n \"word\": \"feature\",\n \"duration\": 0.4,\n \"codes\": [\n 1682,\n 1442,\n 1819,\n 1818,\n 710,\n 1776,\n 1205,\n 646,\n 1688,\n 1572,\n 875,\n 1367,\n 476,\n 1285,\n 460,\n 342,\n 1784,\n 28,\n 1621,\n 1745,\n 1462,\n 988,\n 1780,\n 1697,\n 1249,\n 1348,\n 1120,\n 1590,\n 803,\n 1205\n ]\n }\n ]\n}" }, { "path": "default_speakers/chinenye.json", "content": "{\n \"text\": \"and once I got that out of the way\",\n \"words\": [\n {\n \"word\": \"and\",\n \"duration\": 1.18,\n \"codes\": [\n 1073,\n 1804,\n 1510,\n 1562,\n 377,\n 1287,\n 1615,\n 175,\n 631,\n 1702,\n 1700,\n 1590,\n 1158,\n 1676,\n 758,\n 1727,\n 1548,\n 1464,\n 1605,\n 1469,\n 1291,\n 1755,\n 1656,\n 1323,\n 1372,\n 269,\n 1252,\n 1466,\n 1677,\n 1192,\n 1220,\n 1815,\n 1658,\n 1818,\n 1514,\n 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\"word\": \"that\",\n \"duration\": 0.24,\n \"codes\": [\n 1773,\n 1004,\n 1796,\n 907,\n 239,\n 1804,\n 565,\n 1432,\n 1534,\n 1718,\n 1643,\n 1432,\n 1447,\n 1273,\n 1824,\n 1657,\n 1776,\n 1651\n ]\n },\n {\n \"word\": \"if\",\n \"duration\": 0.12,\n \"codes\": [\n 1649,\n 1620,\n 1342,\n 176,\n 1773,\n 178,\n 1710,\n 1710,\n 1521\n ]\n },\n {\n \"word\": \"you\",\n \"duration\": 0.16,\n \"codes\": [\n 959,\n 1728,\n 1651,\n 361,\n 822,\n 1661,\n 1341,\n 780,\n 1518,\n 335,\n 452,\n 736\n ]\n },\n {\n \"word\": \"say\",\n \"duration\": 0.14,\n \"codes\": [\n 372,\n 1217,\n 713,\n 848,\n 1140,\n 1420,\n 1549,\n 483,\n 125,\n 1353\n ]\n }\n ]\n}" }, { "path": "default_speakers/onye.json", "content": "{\n \"text\": \"out to another level also going through in the shop chop scotch bonnet peppers\",\n \"words\": [\n {\n \"word\": \"out\",\n \"duration\": 0.34,\n \"codes\": [\n 546,\n 416,\n 1519,\n 1673,\n 1806,\n 1015,\n 693,\n 1447,\n 9,\n 1306,\n 1485,\n 1477,\n 1178,\n 1543,\n 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1526,\n 1662,\n 1728,\n 1016,\n 1796\n ]\n },\n {\n \"word\": \"going\",\n \"duration\": 0.26,\n \"codes\": [\n 1789,\n 1291,\n 1209,\n 828,\n 1452,\n 1749,\n 1052,\n 1460,\n 1783,\n 1656,\n 1542,\n 1281,\n 1710,\n 1716,\n 1404,\n 1734,\n 495,\n 1624,\n 1747\n ]\n },\n {\n \"word\": \"through\",\n \"duration\": 0.34,\n \"codes\": [\n 1465,\n 1664,\n 1786,\n 231,\n 1826,\n 1318,\n 1494,\n 1505,\n 1063,\n 1311,\n 1656,\n 1265,\n 1720,\n 1226,\n 940,\n 1490,\n 1447,\n 1730,\n 1348,\n 1637,\n 1118,\n 1710,\n 841,\n 795,\n 298,\n 1216\n ]\n },\n {\n \"word\": \"in\",\n \"duration\": 0.42,\n \"codes\": [\n 899,\n 1240,\n 869,\n 679,\n 1343,\n 1280,\n 1681,\n 1221,\n 1632,\n 1221,\n 1479,\n 1431,\n 1623,\n 1372,\n 1722,\n 1494,\n 1011,\n 1636,\n 957,\n 1661,\n 939,\n 1772,\n 1096,\n 1688,\n 1537,\n 1360,\n 1734,\n 1595,\n 1781,\n 1284,\n 1413\n ]\n },\n {\n \"word\": \"the\",\n \"duration\": 1.08,\n \"codes\": [\n 1701,\n 1447,\n 1328,\n 1690,\n 1281,\n 1401,\n 700,\n 1295,\n 1494,\n 1326,\n 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1511,\n 1683,\n 1779,\n 1660,\n 183,\n 1535,\n 416\n ]\n },\n {\n \"word\": \"scotch\",\n \"duration\": 0.4,\n \"codes\": [\n 1518,\n 1679,\n 0,\n 1695,\n 1682,\n 1098,\n 1764,\n 1256,\n 1808,\n 1609,\n 1745,\n 1318,\n 632,\n 1197,\n 271,\n 1683,\n 1774,\n 1824,\n 1783,\n 1671,\n 1805,\n 22,\n 631,\n 117,\n 1345,\n 800,\n 1707,\n 1466,\n 1005,\n 1462\n ]\n },\n {\n \"word\": \"bonnet\",\n \"duration\": 0.34,\n \"codes\": [\n 1677,\n 1826,\n 1277,\n 524,\n 1001,\n 789,\n 973,\n 1509,\n 1817,\n 546,\n 1260,\n 1117,\n 782,\n 142,\n 1455,\n 947,\n 1814,\n 1815,\n 0,\n 1538,\n 1766,\n 1744,\n 1824,\n 239,\n 1710\n ]\n },\n {\n \"word\": \"peppers\",\n \"duration\": 0.5,\n \"codes\": [\n 1817,\n 1287,\n 1769,\n 1309,\n 446,\n 1173,\n 1183,\n 375,\n 1342,\n 1815,\n 1382,\n 1685,\n 1797,\n 1351,\n 1798,\n 1631,\n 749,\n 1717,\n 1324,\n 1147,\n 1186,\n 955,\n 577,\n 1736,\n 827,\n 1240,\n 1484,\n 847,\n 1661,\n 1475,\n 1287,\n 1535,\n 595,\n 1286,\n 1734,\n 1256,\n 319,\n 1688\n ]\n }\n ]\n}" }, { "path": "default_speakers/osagie.json", "content": "{\n \"text\": \"do Charlotte Douglas shallots be me shut up dummy Libby shallots foolish storms\",\n \"words\": [\n {\n \"word\": \"do\",\n \"duration\": 1.18,\n \"codes\": [\n 1798,\n 858,\n 1653,\n 1400,\n 1441,\n 1810,\n 1180,\n 892,\n 1487,\n 380,\n 208,\n 452,\n 181,\n 714,\n 521,\n 152,\n 1180,\n 2,\n 142,\n 756,\n 208,\n 874,\n 380,\n 565,\n 422,\n 656,\n 81,\n 860,\n 146,\n 1042,\n 1685,\n 1580,\n 50,\n 137,\n 132,\n 170,\n 1633,\n 648,\n 1819,\n 898,\n 1247,\n 1646,\n 1491,\n 438,\n 85,\n 46,\n 170,\n 664,\n 2,\n 236,\n 65,\n 100,\n 393,\n 324,\n 170,\n 1499,\n 1619,\n 519,\n 123,\n 798,\n 79,\n 1447,\n 132,\n 146,\n 779,\n 380,\n 221,\n 1588,\n 228,\n 1443,\n 152,\n 1366,\n 1441,\n 189,\n 320,\n 1387,\n 368,\n 1599,\n 295,\n 65,\n 1353,\n 13,\n 920,\n 1341,\n 55,\n 315,\n 1542,\n 315\n ]\n },\n {\n \"word\": \"charlotte\",\n \"duration\": 0.42,\n \"codes\": [\n 543,\n 769,\n 69,\n 714,\n 725,\n 212,\n 374,\n 1439,\n 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153,\n 127,\n 162,\n 782,\n 932,\n 1023,\n 1262,\n 343,\n 1728,\n 502,\n 1401,\n 996,\n 350,\n 1445,\n 856,\n 298,\n 48,\n 1698,\n 1470,\n 1736,\n 26,\n 1342,\n 328,\n 372,\n 1451\n ]\n },\n {\n \"word\": \"shallots\",\n \"duration\": 0.4,\n \"codes\": [\n 7,\n 50,\n 519,\n 1221,\n 212,\n 238,\n 1083,\n 844,\n 333,\n 182,\n 472,\n 839,\n 609,\n 656,\n 208,\n 291,\n 1234,\n 1678,\n 1151,\n 867,\n 290,\n 546,\n 848,\n 1700,\n 1740,\n 26,\n 1617,\n 1238,\n 183,\n 1693\n ]\n },\n {\n \"word\": \"foolish\",\n \"duration\": 0.38,\n \"codes\": [\n 863,\n 176,\n 1546,\n 1470,\n 1435,\n 716,\n 1460,\n 1013,\n 217,\n 1374,\n 736,\n 91,\n 959,\n 767,\n 1678,\n 1541,\n 903,\n 362,\n 1336,\n 1345,\n 546,\n 848,\n 253,\n 335,\n 510,\n 69,\n 546,\n 1166,\n 1677\n ]\n },\n {\n \"word\": \"storms\",\n \"duration\": 0.4,\n \"codes\": [\n 939,\n 1361,\n 1719,\n 1428,\n 1691,\n 319,\n 1596,\n 236,\n 757,\n 1625,\n 123,\n 1297,\n 55,\n 132,\n 708,\n 92,\n 1344,\n 848,\n 1232,\n 518,\n 695,\n 1726,\n 1502,\n 1759,\n 363,\n 1751,\n 1524,\n 409,\n 189,\n 0\n ]\n }\n ]\n}" }, { "path": "default_speakers/regina.json", "content": "{\n \"text\": \"was just like is that what is amazing to you your marriage is\",\n \"words\": [\n {\n \"word\": \"was\",\n \"duration\": 1.02,\n \"codes\": [\n 1514,\n 571,\n 892,\n 386,\n 186,\n 1403,\n 1082,\n 636,\n 851,\n 1287,\n 1678,\n 1166,\n 162,\n 1345,\n 282,\n 104,\n 1345,\n 329,\n 637,\n 844,\n 537,\n 1366,\n 537,\n 282,\n 1485,\n 537,\n 637,\n 844,\n 537,\n 1710,\n 375,\n 452,\n 1588,\n 537,\n 1382,\n 714,\n 206,\n 333,\n 330,\n 344,\n 281,\n 1523,\n 44,\n 1557,\n 315,\n 479,\n 271,\n 370,\n 110,\n 498,\n 768,\n 560,\n 579,\n 847,\n 961,\n 293,\n 1351,\n 1141,\n 138,\n 1229,\n 2,\n 847,\n 1245,\n 1345,\n 1829,\n 1811,\n 1326,\n 955,\n 1314,\n 137,\n 270,\n 1743,\n 324,\n 1389,\n 1027,\n 863\n ]\n },\n {\n \"word\": \"just\",\n \"duration\": 0.28,\n \"codes\": [\n 333,\n 38,\n 1518,\n 1296,\n 146,\n 1077,\n 1204,\n 665,\n 658,\n 1005,\n 944,\n 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654,\n 281,\n 1578,\n 1557,\n 1346,\n 756\n ]\n },\n {\n \"word\": \"is\",\n \"duration\": 0.06,\n \"codes\": [\n 1337,\n 1662,\n 198,\n 33\n ]\n },\n {\n \"word\": \"that\",\n \"duration\": 0.12,\n \"codes\": [\n 1679,\n 236,\n 934,\n 1056,\n 208,\n 609,\n 860,\n 1318,\n 1340\n ]\n },\n {\n \"word\": \"what\",\n \"duration\": 0.14,\n \"codes\": [\n 1618,\n 806,\n 1068,\n 113,\n 1686,\n 428,\n 230,\n 409,\n 263,\n 415,\n 175\n ]\n },\n {\n \"word\": \"is\",\n \"duration\": 0.1,\n \"codes\": [\n 415,\n 1773,\n 1539,\n 124,\n 1563,\n 700,\n 579\n ]\n },\n {\n \"word\": \"amazing\",\n \"duration\": 0.34,\n \"codes\": [\n 973,\n 695,\n 1247,\n 1737,\n 1609,\n 1664,\n 1006,\n 134,\n 409,\n 416,\n 774,\n 848,\n 1542,\n 10,\n 1441,\n 1539,\n 129,\n 1698,\n 687,\n 1620,\n 1340,\n 749,\n 469,\n 1695,\n 448,\n 448\n ]\n },\n {\n \"word\": \"to\",\n \"duration\": 0.12,\n \"codes\": [\n 189,\n 198,\n 124,\n 1753,\n 510,\n 1825,\n 856,\n 1441,\n 1688\n ]\n },\n {\n \"word\": \"you\",\n \"duration\": 1.62,\n \"codes\": [\n 1552,\n 1546,\n 1698,\n 166,\n 101,\n 1457,\n 137,\n 864,\n 790,\n 794,\n 1615,\n 454,\n 1512,\n 328,\n 634,\n 1578,\n 409,\n 1592,\n 176,\n 1441,\n 1644,\n 356,\n 1641,\n 1580,\n 510,\n 1609,\n 407,\n 882,\n 1580,\n 218,\n 1616,\n 865,\n 409,\n 1570,\n 1376,\n 1734,\n 34,\n 687,\n 1592,\n 556,\n 640,\n 1592,\n 6,\n 1362,\n 4,\n 1546,\n 1302,\n 1376,\n 1570,\n 34,\n 652,\n 180,\n 1569,\n 203,\n 1744,\n 282,\n 945,\n 362,\n 931,\n 1662,\n 631,\n 1580,\n 452,\n 329,\n 725,\n 140,\n 277,\n 1113,\n 537,\n 1332,\n 560,\n 282,\n 1056,\n 270,\n 940,\n 755,\n 860,\n 104,\n 903,\n 537,\n 1310,\n 579,\n 282,\n 848,\n 371,\n 844,\n 1808,\n 400,\n 1772,\n 1166,\n 213,\n 1485,\n 1502,\n 276,\n 1594,\n 1599,\n 1819,\n 1197,\n 441,\n 1318,\n 1237,\n 679,\n 1186,\n 384,\n 609,\n 637,\n 157,\n 609,\n 637,\n 157,\n 790,\n 157,\n 547,\n 452,\n 452,\n 870,\n 162,\n 320,\n 1649,\n 1272,\n 1318,\n 860\n ]\n },\n {\n \"word\": \"your\",\n \"duration\": 0.16,\n \"codes\": [\n 1477,\n 67,\n 113,\n 1149,\n 479,\n 901,\n 1232,\n 295,\n 9,\n 1129,\n 67,\n 1825\n ]\n },\n {\n \"word\": \"marriage\",\n \"duration\": 0.8,\n \"codes\": [\n 529,\n 697,\n 695,\n 1429,\n 282,\n 626,\n 1355,\n 192,\n 1671,\n 100,\n 95,\n 1310,\n 388,\n 1155,\n 1494,\n 104,\n 104,\n 587,\n 1156,\n 67,\n 57,\n 1437,\n 697,\n 714,\n 1221,\n 1443,\n 2,\n 1357,\n 931,\n 931,\n 1298,\n 388,\n 1136,\n 1604,\n 428,\n 1240,\n 1698,\n 65,\n 1272,\n 128,\n 755,\n 79,\n 794,\n 1698,\n 1518,\n 1546,\n 1696,\n 448,\n 233,\n 1599,\n 1732,\n 1240,\n 110,\n 775,\n 483,\n 100,\n 1075,\n 346,\n 863,\n 1498\n ]\n },\n {\n \"word\": \"is\",\n \"duration\": 0.1,\n \"codes\": [\n 631,\n 18,\n 679,\n 430,\n 176,\n 10,\n 52\n ]\n }\n ]\n}" }, { "path": "default_speakers/remi.json", "content": "{\n \"text\": \"animal noral human being\",\n \"words\": [\n {\n \"word\": \"animal\",\n \"duration\": 2.79,\n \"codes\": [\n 1679,\n 1711,\n 714,\n 1588,\n 906,\n 725,\n 789,\n 456,\n 79,\n 230,\n 1127,\n 532,\n 200,\n 834,\n 29,\n 753,\n 1420,\n 595,\n 997,\n 557,\n 205,\n 488,\n 775,\n 63,\n 1520,\n 1600,\n 1394,\n 1811,\n 1715,\n 473,\n 805,\n 128,\n 502,\n 1353,\n 1636,\n 1832,\n 182,\n 381,\n 281,\n 1540,\n 748,\n 1341,\n 1744,\n 374,\n 1767,\n 182,\n 621,\n 495,\n 234,\n 909,\n 1383,\n 92,\n 1545,\n 1394,\n 1794,\n 1641,\n 319,\n 1452,\n 1240,\n 217,\n 1815,\n 388,\n 828,\n 1664,\n 184,\n 1239,\n 319,\n 1469,\n 1810,\n 36,\n 1019,\n 1451,\n 774,\n 1819,\n 1521,\n 761,\n 23,\n 1609,\n 273,\n 52,\n 1670,\n 524,\n 813,\n 806,\n 79,\n 1141,\n 1677,\n 138,\n 1409,\n 1468,\n 1633,\n 1573,\n 782,\n 1655,\n 1669,\n 1239,\n 458,\n 1495,\n 258,\n 544,\n 1532,\n 1567,\n 1627,\n 1641,\n 851,\n 1573,\n 1569,\n 265,\n 686,\n 72,\n 151,\n 342,\n 194,\n 75,\n 419,\n 342,\n 542,\n 419,\n 75,\n 342,\n 246,\n 75,\n 342,\n 246,\n 56,\n 161,\n 246,\n 442,\n 161,\n 56,\n 156,\n 420,\n 161,\n 75,\n 219,\n 194,\n 56,\n 156,\n 220,\n 453,\n 156,\n 1019,\n 490,\n 1415,\n 742,\n 1533,\n 412,\n 828,\n 138,\n 1487,\n 128,\n 660,\n 1339,\n 882,\n 154,\n 1533,\n 47,\n 312,\n 730,\n 1087,\n 764,\n 346,\n 1394,\n 179,\n 959,\n 1344,\n 324,\n 1457,\n 388,\n 57,\n 514,\n 1323,\n 631,\n 6,\n 479,\n 815,\n 1599,\n 384,\n 952,\n 1650,\n 57,\n 314,\n 320,\n 787,\n 1488,\n 147,\n 203,\n 1078,\n 192,\n 1663,\n 236,\n 1501,\n 270,\n 1280,\n 716,\n 631,\n 1584,\n 1605,\n 1779,\n 1239,\n 363,\n 1437,\n 430,\n 1554,\n 1069,\n 189,\n 319,\n 856,\n 143\n ]\n },\n {\n \"word\": \"noral\",\n \"duration\": 0.56,\n \"codes\": [\n 1831,\n 201,\n 1674,\n 1707,\n 1807,\n 487,\n 1577,\n 1394,\n 1341,\n 412,\n 814,\n 205,\n 1633,\n 79,\n 1267,\n 1625,\n 315,\n 1649,\n 4,\n 780,\n 368,\n 592,\n 1633,\n 592,\n 1431,\n 1563,\n 599,\n 176,\n 10,\n 725,\n 1468,\n 76,\n 593,\n 714,\n 146,\n 974,\n 725,\n 549,\n 57,\n 1068,\n 1729,\n 52\n ]\n },\n {\n \"word\": \"human\",\n \"duration\": 0.82,\n \"codes\": [\n 1552,\n 233,\n 298,\n 949,\n 1636,\n 380,\n 363,\n 1520,\n 1768,\n 85,\n 483,\n 876,\n 125,\n 153,\n 564,\n 200,\n 1221,\n 803,\n 1712,\n 117,\n 804,\n 688,\n 787,\n 1345,\n 592,\n 291,\n 472,\n 158,\n 132,\n 1827,\n 617,\n 157,\n 36,\n 1186,\n 1008,\n 324,\n 961,\n 644,\n 179,\n 931,\n 1400,\n 688,\n 1015,\n 488,\n 532,\n 500,\n 952,\n 945,\n 29,\n 1497,\n 529,\n 749,\n 1733,\n 439,\n 63,\n 1773,\n 1527,\n 1622,\n 728,\n 1613,\n 1274,\n 136\n ]\n },\n {\n \"word\": \"being\",\n \"duration\": 0.54,\n \"codes\": [\n 546,\n 1287,\n 166,\n 315,\n 1678,\n 882,\n 1753,\n 1018,\n 1449,\n 1581,\n 298,\n 1710,\n 1799,\n 1772,\n 1406,\n 1538,\n 1728,\n 1657,\n 1778,\n 182,\n 921,\n 217,\n 1615,\n 133,\n 217,\n 1516,\n 1830,\n 844,\n 1584,\n 338,\n 1639,\n 644,\n 417,\n 774,\n 1724,\n 648,\n 749,\n 4,\n 315,\n 1497\n ]\n }\n ]\n}" }, { "path": "default_speakers/saheed.json", "content": "{\n \"text\": \"Hello! My name is Saheed azeez and I am testing the audio feature\",\n \"words\": [\n {\n \"word\": \"hello\",\n \"duration\": 2.38,\n \"codes\": [\n 219,\n 244,\n 244,\n 167,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 453,\n 244,\n 219,\n 139,\n 966,\n 1099,\n 1299,\n 1433,\n 1128,\n 1266,\n 1517,\n 649,\n 196,\n 1731,\n 1405,\n 830,\n 1771,\n 964,\n 476,\n 1803,\n 584,\n 875,\n 1683,\n 986,\n 363,\n 1489,\n 465,\n 5,\n 1067,\n 606,\n 1590,\n 1397,\n 265,\n 1446,\n 1279,\n 799,\n 1491,\n 1367,\n 606,\n 1593,\n 1279,\n 360,\n 256,\n 1705,\n 1425,\n 58,\n 1210,\n 1357,\n 1379,\n 752,\n 1640,\n 837,\n 734,\n 1787,\n 1406,\n 1052,\n 1796,\n 686,\n 1446,\n 1716,\n 564,\n 595,\n 1716,\n 728,\n 847,\n 732,\n 935,\n 1253,\n 752,\n 1019,\n 1455,\n 564,\n 1492,\n 733,\n 1645,\n 1391,\n 728,\n 1501,\n 1822,\n 1339,\n 1677,\n 1456,\n 807,\n 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1379,\n 1756\n ]\n },\n {\n \"word\": \"saheed\",\n \"duration\": 0.68,\n \"codes\": [\n 1419,\n 1568,\n 1643,\n 1099,\n 1795,\n 970,\n 1184,\n 1498,\n 877,\n 1162,\n 902,\n 1537,\n 1192,\n 1565,\n 1472,\n 1109,\n 1225,\n 1321,\n 1453,\n 1654,\n 1274,\n 1811,\n 1695,\n 946,\n 1631,\n 1590,\n 1152,\n 820,\n 272,\n 1458,\n 1378,\n 240,\n 1421,\n 174,\n 925,\n 1126,\n 1346,\n 1600,\n 1716,\n 258,\n 1611,\n 442,\n 625,\n 1448,\n 246,\n 957,\n 226,\n 338,\n 1190,\n 921,\n 1505\n ]\n },\n {\n \"word\": \"azeez\",\n \"duration\": 0.8,\n \"codes\": [\n 1195,\n 646,\n 1505,\n 1014,\n 250,\n 837,\n 729,\n 121,\n 1715,\n 1446,\n 1430,\n 1608,\n 1575,\n 1057,\n 1643,\n 1514,\n 1795,\n 893,\n 1718,\n 1383,\n 840,\n 1802,\n 426,\n 1414,\n 1573,\n 1784,\n 1285,\n 852,\n 1246,\n 896,\n 1744,\n 1299,\n 495,\n 1796,\n 1570,\n 1665,\n 505,\n 888,\n 1654,\n 343,\n 1120,\n 1474,\n 16,\n 1035,\n 505,\n 1699,\n 862,\n 692,\n 1623,\n 633,\n 566,\n 1037,\n 342,\n 950,\n 261,\n 729,\n 1317,\n 177,\n 1213,\n 1333\n ]\n },\n {\n \"word\": \"and\",\n \"duration\": 0.34,\n \"codes\": [\n 908,\n 1203,\n 1683,\n 926,\n 1278,\n 564,\n 1067,\n 1003,\n 90,\n 459,\n 568,\n 272,\n 1117,\n 1396,\n 1411,\n 1233,\n 193,\n 1197,\n 970,\n 1065,\n 1611,\n 883,\n 1216,\n 1776,\n 747\n ]\n },\n {\n \"word\": \"i\",\n \"duration\": 0.06,\n \"codes\": [\n 924,\n 1628,\n 988,\n 1116,\n 1388\n ]\n },\n {\n \"word\": \"am\",\n \"duration\": 0.18,\n \"codes\": [\n 1199,\n 1188,\n 593,\n 953,\n 459,\n 272,\n 869,\n 1321,\n 145,\n 1306,\n 272,\n 406,\n 1479\n ]\n },\n {\n \"word\": \"testing\",\n \"duration\": 0.44,\n \"codes\": [\n 237,\n 1003,\n 1638,\n 638,\n 1180,\n 1666,\n 811,\n 1178,\n 1565,\n 814,\n 1211,\n 1654,\n 1779,\n 1313,\n 1619,\n 1684,\n 1230,\n 419,\n 891,\n 28,\n 1231,\n 1379,\n 729,\n 1682,\n 338,\n 1468,\n 136,\n 1630,\n 1215,\n 251,\n 1464,\n 781,\n 598\n ]\n },\n {\n \"word\": \"the\",\n \"duration\": 0.22,\n \"codes\": [\n 555,\n 692,\n 663,\n 1632,\n 905,\n 807,\n 1085,\n 752,\n 1433,\n 392,\n 921,\n 1820,\n 363,\n 987,\n 1328,\n 734,\n 1063\n ]\n },\n {\n \"word\": \"audio\",\n \"duration\": 0.34,\n \"codes\": [\n 1294,\n 814,\n 1423,\n 1750,\n 747,\n 672,\n 651,\n 250,\n 1478,\n 37,\n 1760,\n 1021,\n 850,\n 58,\n 438,\n 953,\n 1668,\n 771,\n 729,\n 1456,\n 322,\n 591,\n 1474,\n 1440,\n 1170\n ]\n },\n {\n \"word\": \"feature\",\n \"duration\": 0.4,\n \"codes\": [\n 332,\n 1333,\n 1146,\n 1025,\n 19,\n 501,\n 169,\n 1250,\n 734,\n 1629,\n 1383,\n 355,\n 1747,\n 584,\n 237,\n 1428,\n 240,\n 1298,\n 999,\n 1338,\n 1438,\n 1727,\n 987,\n 1455,\n 792,\n 932,\n 1199,\n 355,\n 1185,\n 772\n ]\n }\n ]\n}\n" }, { "path": "default_speakers/tayo.json", "content": "{\n \"text\": \"and enjoy ourselves we need more parties let party start again now we know\",\n \"words\": [\n {\n \"word\": \"and\",\n \"duration\": 0.5,\n \"codes\": [\n 82,\n 1201,\n 329,\n 992,\n 908,\n 847,\n 925,\n 1666,\n 1057,\n 1266,\n 1448,\n 1737,\n 1251,\n 1031,\n 1759,\n 1459,\n 1094,\n 1750,\n 1739,\n 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181,\n 738,\n 1641\n ]\n },\n {\n \"word\": \"need\",\n \"duration\": 0.14,\n \"codes\": [\n 1603,\n 177,\n 195,\n 65,\n 1600,\n 104,\n 143,\n 1574,\n 1416,\n 160,\n 50\n ]\n },\n {\n \"word\": \"more\",\n \"duration\": 0.18,\n \"codes\": [\n 48,\n 1597,\n 39,\n 1414,\n 74,\n 1192,\n 84,\n 1345,\n 748,\n 1269,\n 1672,\n 686,\n 1820,\n 1442\n ]\n },\n {\n \"word\": \"parties\",\n \"duration\": 0.56,\n \"codes\": [\n 1640,\n 1030,\n 138,\n 147,\n 413,\n 110,\n 282,\n 1633,\n 1659,\n 1524,\n 176,\n 350,\n 137,\n 1004,\n 92,\n 1240,\n 1521,\n 1376,\n 502,\n 1558,\n 592,\n 473,\n 1021,\n 1805,\n 1346,\n 1393,\n 1759,\n 1786,\n 231,\n 1728,\n 117,\n 1366,\n 1754,\n 1073,\n 1786,\n 1354,\n 1532,\n 1572,\n 1754,\n 16,\n 257,\n 273\n ]\n },\n {\n \"word\": \"let\",\n \"duration\": 0.16,\n \"codes\": [\n 1312,\n 961,\n 372,\n 212,\n 1253,\n 115,\n 656,\n 1374,\n 78,\n 1322,\n 1284,\n 343\n ]\n },\n {\n \"word\": \"party\",\n \"duration\": 0.24,\n \"codes\": [\n 1572,\n 1662,\n 25,\n 390,\n 892,\n 212,\n 637,\n 576,\n 176,\n 1702,\n 640,\n 276,\n 52,\n 648,\n 577,\n 1240,\n 276,\n 155\n ]\n },\n {\n \"word\": \"start\",\n \"duration\": 0.3,\n \"codes\": [\n 213,\n 356,\n 1603,\n 1284,\n 1442,\n 1599,\n 705,\n 82,\n 65,\n 764,\n 349,\n 370,\n 856,\n 1524,\n 1508,\n 209,\n 495,\n 1552,\n 50,\n 1588,\n 863,\n 63\n ]\n },\n {\n \"word\": \"again\",\n \"duration\": 0.3,\n \"codes\": [\n 1267,\n 273,\n 298,\n 1409,\n 101,\n 1548,\n 733,\n 625,\n 1728,\n 1283,\n 286,\n 1645,\n 1363,\n 368,\n 153,\n 289,\n 716,\n 1756,\n 865,\n 1376,\n 688,\n 332,\n 731\n ]\n },\n {\n \"word\": \"now\",\n \"duration\": 0.44,\n \"codes\": [\n 983,\n 385,\n 1002,\n 806,\n 1798,\n 95,\n 1776,\n 825,\n 1790,\n 737,\n 1595,\n 907,\n 932,\n 1786,\n 626,\n 831,\n 1823,\n 1680,\n 1780,\n 1502,\n 1206,\n 1078,\n 47,\n 829,\n 868,\n 69,\n 277,\n 429,\n 125,\n 132,\n 14,\n 1497,\n 444\n ]\n },\n {\n \"word\": \"we\",\n \"duration\": 1.32,\n \"codes\": [\n 1692,\n 648,\n 481,\n 155,\n 483,\n 126,\n 1283,\n 12,\n 108,\n 429,\n 828,\n 128,\n 1161,\n 725,\n 155,\n 107,\n 1610,\n 228,\n 1492,\n 1560,\n 368,\n 1138,\n 810,\n 1572,\n 1562,\n 320,\n 112,\n 520,\n 52,\n 49,\n 1008,\n 1635,\n 1728,\n 1523,\n 62,\n 190,\n 648,\n 592,\n 384,\n 969,\n 1441,\n 519,\n 1536,\n 1571,\n 1587,\n 1539,\n 15,\n 1156,\n 376,\n 1022,\n 642,\n 483,\n 1794,\n 1335,\n 1712,\n 1449,\n 529,\n 1558,\n 1463,\n 1559,\n 1706,\n 1460,\n 249,\n 1308,\n 293,\n 529,\n 841,\n 201,\n 1256,\n 931,\n 132,\n 1173,\n 479,\n 286,\n 1075,\n 153,\n 13,\n 1503,\n 398,\n 415,\n 432,\n 7,\n 183,\n 103,\n 409,\n 736,\n 15,\n 940,\n 1459,\n 15,\n 1631,\n 1580,\n 1773,\n 624,\n 1417,\n 926,\n 531,\n 1159,\n 1257\n ]\n },\n {\n \"word\": \"know\",\n \"duration\": 0.44,\n \"codes\": [\n 777,\n 1240,\n 446,\n 303,\n 153,\n 263,\n 1402,\n 317,\n 1365,\n 481,\n 848,\n 1280,\n 354,\n 1415,\n 245,\n 408,\n 462,\n 466,\n 253,\n 943,\n 472,\n 215,\n 143,\n 519,\n 202,\n 1389,\n 1608,\n 714,\n 1599,\n 399,\n 944,\n 124,\n 844\n ]\n }\n ]\n}" }, { "path": "default_speakers/umar.json", "content": "{\n \"text\": \"that i'd like to share with everybody in the world yes sometimes you go all the way\",\n \"words\": [\n {\n \"word\": \"that\",\n \"duration\": 0.48,\n \"codes\": [\n 519,\n 848,\n 1374,\n 416,\n 940,\n 1445,\n 416,\n 753,\n 1616,\n 774,\n 803,\n 1697,\n 1541,\n 1047,\n 200,\n 462,\n 1417,\n 1313,\n 1296,\n 184,\n 1396,\n 1568,\n 1416,\n 1444,\n 1631,\n 1463,\n 702,\n 1831,\n 1564,\n 1374,\n 1580,\n 1643,\n 1681,\n 1660,\n 1124,\n 1720\n ]\n },\n {\n \"word\": \"id\",\n \"duration\": 0.38,\n \"codes\": [\n 4,\n 705,\n 1534,\n 1290,\n 1661,\n 302,\n 1798,\n 844,\n 197,\n 1027,\n 1606,\n 903,\n 1414,\n 794,\n 871,\n 882,\n 941,\n 1310,\n 871,\n 1247,\n 1140,\n 1247,\n 718,\n 1422,\n 1509,\n 1678,\n 1093,\n 1734\n ]\n },\n {\n \"word\": \"like\",\n \"duration\": 0.18,\n \"codes\": [\n 647,\n 1824,\n 474,\n 1111,\n 599,\n 221,\n 1435,\n 822,\n 1409,\n 1717,\n 1748,\n 1550,\n 1738,\n 1717\n ]\n },\n {\n \"word\": \"to\",\n \"duration\": 0.14,\n \"codes\": [\n 1535,\n 231,\n 1794,\n 1553,\n 1351,\n 1365,\n 1296,\n 1781,\n 1599,\n 1082\n ]\n },\n {\n \"word\": \"share\",\n \"duration\": 0.18,\n \"codes\": [\n 1737,\n 0,\n 979,\n 1688,\n 546,\n 1807,\n 319,\n 252,\n 1805,\n 714,\n 580,\n 1524,\n 798,\n 1779\n ]\n },\n {\n \"word\": \"with\",\n \"duration\": 0.14,\n \"codes\": [\n 1698,\n 702,\n 966,\n 1461,\n 127,\n 1681,\n 85,\n 1741,\n 1588,\n 718\n ]\n },\n {\n \"word\": \"everybody\",\n \"duration\": 0.4,\n \"codes\": [\n 1600,\n 806,\n 1770,\n 1078,\n 1727,\n 679,\n 1569,\n 1452,\n 1685,\n 774,\n 1598,\n 1382,\n 1520,\n 1786,\n 1702,\n 1607,\n 1747,\n 828,\n 1553,\n 983,\n 1103,\n 882,\n 1427,\n 1679,\n 1613,\n 1636,\n 1433,\n 519,\n 853,\n 1451\n ]\n },\n {\n \"word\": \"in\",\n \"duration\": 0.06,\n \"codes\": [\n 1369,\n 1654,\n 1581,\n 1600,\n 1452\n ]\n },\n {\n \"word\": \"the\",\n \"duration\": 0.12,\n \"codes\": [\n 1241,\n 1769,\n 678,\n 1751,\n 1280,\n 1711,\n 1663,\n 1772,\n 1655\n ]\n },\n {\n \"word\": \"world\",\n \"duration\": 0.74,\n \"codes\": [\n 973,\n 1231,\n 1015,\n 1052,\n 1415,\n 721,\n 1822,\n 825,\n 1076,\n 1431,\n 1357,\n 1389,\n 744,\n 1263,\n 1525,\n 1794,\n 319,\n 1678,\n 1732,\n 1395,\n 1695,\n 1827,\n 1059,\n 1719,\n 1675,\n 1714,\n 1635,\n 1466,\n 1730,\n 1750,\n 1395,\n 1525,\n 1827,\n 1313,\n 1440,\n 1447,\n 1292,\n 1762,\n 1226,\n 1418,\n 1750,\n 719,\n 1549,\n 1761,\n 1459,\n 1717,\n 1800,\n 1404,\n 1702,\n 1795,\n 1711,\n 1789,\n 1808,\n 1759,\n 385,\n 415\n ]\n },\n {\n \"word\": \"yes\",\n \"duration\": 0.32,\n \"codes\": [\n 302,\n 1704,\n 485,\n 983,\n 234,\n 63,\n 462,\n 483,\n 82,\n 827,\n 999,\n 1143,\n 102,\n 1655,\n 117,\n 1619,\n 519,\n 1217,\n 1518,\n 1476,\n 333,\n 1660,\n 1238,\n 1679\n ]\n },\n {\n \"word\": \"sometimes\",\n \"duration\": 0.58,\n \"codes\": [\n 1287,\n 546,\n 1552,\n 1736,\n 1647,\n 836,\n 575,\n 354,\n 1156,\n 1264,\n 1194,\n 1761,\n 1629,\n 1452,\n 1241,\n 1394,\n 856,\n 1313,\n 1653,\n 736,\n 556,\n 1387,\n 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"content": "{\n \"text\": \"Idan hira tayi \\u0257a\\u0257i bana son na tashi.\",\n \"words\": [\n {\n \"word\": \"idan\",\n \"duration\": \"0.52\",\n \"codes\": [\n 165,\n 338,\n 781,\n 661,\n 601,\n 691,\n 1154,\n 762,\n 691,\n 523,\n 641,\n 378,\n 1464,\n 38,\n 1280,\n 243,\n 1784,\n 195,\n 5,\n 1679,\n 77,\n 530,\n 1527,\n 270,\n 243,\n 374,\n 200,\n 157,\n 152,\n 228,\n 768,\n 743,\n 104,\n 221,\n 968,\n 479,\n 321,\n 1679,\n 1279\n ]\n },\n {\n \"word\": \"hira\",\n \"duration\": \"0.38\",\n \"codes\": [\n 1587,\n 1544,\n 683,\n 92,\n 1255,\n 46,\n 106,\n 636,\n 320,\n 53,\n 249,\n 123,\n 1140,\n 1290,\n 93,\n 553,\n 0,\n 1192,\n 210,\n 587,\n 1184,\n 764,\n 215,\n 221,\n 2,\n 1115,\n 1079,\n 1033\n ]\n },\n {\n \"word\": \"tayi\",\n \"duration\": \"0.38\",\n \"codes\": [\n 447,\n 1292,\n 198,\n 50,\n 1439,\n 1191,\n 1399,\n 106,\n 880,\n 844,\n 306,\n 466,\n 74,\n 260,\n 152,\n 723,\n 723,\n 687,\n 306,\n 195,\n 648,\n 466,\n 30,\n 1110,\n 637,\n 384,\n 1131,\n 342,\n 392\n ]\n },\n {\n \"word\": \"dadi\",\n \"duration\": \"0.38\",\n \"codes\": [\n 751,\n 412,\n 212,\n 306,\n 388,\n 589,\n 446,\n 479,\n 880,\n 768,\n 467,\n 699,\n 128,\n 665,\n 882,\n 908,\n 171,\n 1146,\n 1297,\n 687,\n 901,\n 1110,\n 153,\n 386,\n 1330,\n 1283,\n 1181,\n 1070,\n 766\n ]\n },\n {\n \"word\": \"bana\",\n \"duration\": \"0.46\",\n \"codes\": [\n 534,\n 1440,\n 1102,\n 1194,\n 474,\n 252,\n 39,\n 367,\n 116,\n 212,\n 36,\n 115,\n 76,\n 1173,\n 931,\n 1285,\n 1630,\n 678,\n 1087,\n 208,\n 1055,\n 441,\n 545,\n 324,\n 1192,\n 179,\n 1147,\n 897,\n 1387,\n 1283,\n 10,\n 1,\n 654,\n 863,\n 103\n ]\n },\n {\n \"word\": \"son\",\n \"duration\": \"0.22\",\n \"codes\": [\n 198,\n 507,\n 1477,\n 915,\n 215,\n 267,\n 1232,\n 1041,\n 569,\n 1596,\n 1759,\n 229,\n 901,\n 1774,\n 1487,\n 51\n ]\n },\n {\n \"word\": \"na\",\n \"duration\": \"0.16\",\n \"codes\": [\n 251,\n 243,\n 965,\n 215,\n 135,\n 711,\n 105,\n 1350,\n 1556,\n 226,\n 459,\n 68\n ]\n },\n {\n \"word\": \"tashi\",\n 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1184,\n 764,\n 215,\n 221,\n 2,\n 1115,\n 1079,\n 1033\n ]\n },\n {\n \"word\": \"tayi\",\n \"duration\": \"0.38\",\n \"codes\": [\n 447,\n 1292,\n 198,\n 50,\n 1439,\n 1191,\n 1399,\n 106,\n 880,\n 844,\n 306,\n 466,\n 74,\n 260,\n 152,\n 723,\n 723,\n 687,\n 306,\n 195,\n 648,\n 466,\n 30,\n 1110,\n 637,\n 384,\n 1131,\n 342,\n 392\n ]\n },\n {\n \"word\": \"dadi\",\n \"duration\": \"0.38\",\n \"codes\": [\n 751,\n 412,\n 212,\n 306,\n 388,\n 589,\n 446,\n 479,\n 880,\n 768,\n 467,\n 699,\n 128,\n 665,\n 882,\n 908,\n 171,\n 1146,\n 1297,\n 687,\n 901,\n 1110,\n 153,\n 386,\n 1330,\n 1283,\n 1181,\n 1070,\n 766\n ]\n },\n {\n \"word\": \"bana\",\n \"duration\": \"0.46\",\n \"codes\": [\n 534,\n 1440,\n 1102,\n 1194,\n 474,\n 252,\n 39,\n 367,\n 116,\n 212,\n 36,\n 115,\n 76,\n 1173,\n 931,\n 1285,\n 1630,\n 678,\n 1087,\n 208,\n 1055,\n 441,\n 545,\n 324,\n 1192,\n 179,\n 1147,\n 897,\n 1387,\n 1283,\n 10,\n 1,\n 654,\n 863,\n 103\n ]\n },\n {\n \"word\": \"son\",\n \"duration\": \"0.22\",\n \"codes\": [\n 198,\n 507,\n 1477,\n 915,\n 215,\n 267,\n 1232,\n 1041,\n 569,\n 1596,\n 1759,\n 229,\n 901,\n 1774,\n 1487,\n 51\n ]\n },\n {\n \"word\": \"na\",\n \"duration\": \"0.16\",\n \"codes\": [\n 251,\n 243,\n 965,\n 215,\n 135,\n 711,\n 105,\n 1350,\n 1556,\n 226,\n 459,\n 68\n ]\n },\n {\n \"word\": \"tashi\",\n \"duration\": \"0.42\",\n \"codes\": [\n 20,\n 502,\n 610,\n 179,\n 711,\n 800,\n 424,\n 352,\n 102,\n 569,\n 67,\n 262,\n 855,\n 413,\n 63,\n 701,\n 1719,\n 262,\n 383,\n 1166,\n 358,\n 1331,\n 596,\n 383,\n 1351,\n 96,\n 1170,\n 1061,\n 1059,\n 1392,\n 328,\n 1471\n ]\n }\n ]\n}" }, { "path": "default_speakers_local/hausa_male1.json", "content": "{\n \"text\": \"Eh, mun za\\u0253i yin wasan kwaikwayo don nuna al'adunmu yayin ranar al'ada.\",\n \"words\": [\n {\n \"word\": \"eh\",\n \"duration\": \"0.86\",\n \"codes\": [\n 165,\n 226,\n 1145,\n 284,\n 77,\n 187,\n 459,\n 77,\n 691,\n 278,\n 643,\n 247,\n 156,\n 204,\n 89,\n 1247,\n 52,\n 1350,\n 433,\n 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]\n },\n {\n \"word\": \"wasan\",\n \"duration\": \"0.44\",\n \"codes\": [\n 1070,\n 520,\n 973,\n 754,\n 83,\n 997,\n 1253,\n 982,\n 359,\n 537,\n 1115,\n 1677,\n 1358,\n 1250,\n 1403,\n 1637,\n 881,\n 382,\n 1754,\n 589,\n 1131,\n 88,\n 1256,\n 988,\n 83,\n 672,\n 644,\n 847,\n 322,\n 983,\n 1305,\n 31,\n 967\n ]\n },\n {\n \"word\": \"kwaikwayo\",\n \"duration\": \"0.58\",\n \"codes\": [\n 1071,\n 1003,\n 1811,\n 684,\n 1210,\n 553,\n 1535,\n 491,\n 398,\n 222,\n 315,\n 439,\n 205,\n 174,\n 1742,\n 1373,\n 259,\n 1185,\n 1787,\n 516,\n 1440,\n 646,\n 1402,\n 267,\n 1677,\n 553,\n 344,\n 429,\n 202,\n 389,\n 782,\n 662,\n 388,\n 177,\n 553,\n 1413,\n 491,\n 554,\n 222,\n 759,\n 111,\n 1719,\n 1305,\n 437\n ]\n },\n {\n \"word\": \"don\",\n \"duration\": \"0.24\",\n \"codes\": [\n 144,\n 824,\n 90,\n 637,\n 439,\n 138,\n 593,\n 609,\n 617,\n 1247,\n 444,\n 793,\n 600,\n 1425,\n 1379,\n 283,\n 995,\n 1804\n ]\n },\n {\n \"word\": \"nuna\",\n \"duration\": \"0.40\",\n \"codes\": [\n 389,\n 669,\n 1804,\n 506,\n 1668,\n 1621,\n 341,\n 913,\n 1495,\n 1819,\n 112,\n 647,\n 743,\n 1612,\n 506,\n 1320,\n 1648,\n 106,\n 1107,\n 579,\n 326,\n 140,\n 1220,\n 936,\n 661,\n 729,\n 1183,\n 441,\n 797,\n 309\n ]\n },\n {\n \"word\": \"aladunmu\",\n \"duration\": \"0.76\",\n \"codes\": [\n 1260,\n 179,\n 1240,\n 68,\n 753,\n 807,\n 1808,\n 894,\n 140,\n 791,\n 1486,\n 1276,\n 1471,\n 1132,\n 573,\n 797,\n 1307,\n 271,\n 632,\n 1059,\n 699,\n 816,\n 282,\n 908,\n 1240,\n 41,\n 144,\n 1721,\n 322,\n 237,\n 1284,\n 1312,\n 1444,\n 521,\n 593,\n 753,\n 506,\n 1024,\n 439,\n 1142,\n 1790,\n 478,\n 1164,\n 953,\n 1727,\n 1078,\n 564,\n 1665,\n 482,\n 976,\n 910,\n 727,\n 297,\n 677,\n 297,\n 507,\n 1157\n ]\n }\n ]\n}" }, { "path": "default_speakers_local/hausa_male2.json", "content": "{\n \"text\": \"Audu ya hau jirgi a Kaduna.\",\n \"words\": [\n {\n \"word\": \"audu\",\n \"duration\": \"0.75\",\n \"codes\": [\n 165,\n 167,\n 68,\n 567,\n 156,\n 351,\n 337,\n 156,\n 351,\n 337,\n 337,\n 219,\n 584,\n 156,\n 762,\n 334,\n 185,\n 156,\n 334,\n 762,\n 156,\n 337,\n 612,\n 219,\n 691,\n 185,\n 156,\n 204,\n 862,\n 777,\n 589,\n 173,\n 550,\n 128,\n 489,\n 182,\n 74,\n 255,\n 427,\n 1554,\n 945,\n 289,\n 79,\n 875,\n 442,\n 1664,\n 464,\n 230,\n 1500,\n 181,\n 1152,\n 286,\n 103,\n 662,\n 125\n ]\n },\n {\n \"word\": \"ya\",\n \"duration\": \"0.22\",\n \"codes\": [\n 201,\n 1332,\n 67,\n 1041,\n 248,\n 901,\n 352,\n 969,\n 642,\n 105,\n 215,\n 411,\n 408,\n 1235,\n 1212,\n 1264,\n 653\n ]\n },\n {\n \"word\": \"hau\",\n \"duration\": \"0.22\",\n \"codes\": [\n 1083,\n 913,\n 1026,\n 1295,\n 1473,\n 1399,\n 41,\n 629,\n 1081,\n 623,\n 536,\n 890,\n 1554,\n 384,\n 1664,\n 921,\n 325\n ]\n },\n {\n \"word\": \"jirgi\",\n \"duration\": \"0.48\",\n \"codes\": [\n 486,\n 1536,\n 597,\n 1088,\n 1743,\n 1286,\n 340,\n 949,\n 116,\n 1441,\n 1550,\n 28,\n 1073,\n 973,\n 233,\n 1319,\n 733,\n 465,\n 1152,\n 1644,\n 773,\n 1651,\n 175,\n 1281,\n 1563,\n 11,\n 1773,\n 1323,\n 30,\n 10,\n 424,\n 293,\n 1437,\n 1484,\n 1072,\n 370\n ]\n },\n {\n \"word\": \"a\",\n \"duration\": \"0.10\",\n \"codes\": [\n 159,\n 697,\n 53,\n 1040,\n 1256,\n 264,\n 710,\n 1251\n ]\n },\n {\n \"word\": \"kaduna\",\n \"duration\": \"0.44\",\n \"codes\": [\n 1203,\n 764,\n 1473,\n 1156,\n 400,\n 212,\n 1698,\n 1217,\n 145,\n 1569,\n 1151,\n 1056,\n 1700,\n 1527,\n 629,\n 1747,\n 1350,\n 738,\n 1734,\n 55,\n 1595,\n 890,\n 55,\n 1364,\n 203,\n 281,\n 952,\n 1234,\n 452,\n 93,\n 1036,\n 565,\n 969\n ]\n }\n ]\n}" }, { "path": "default_speakers_local/igbo_female1.json", "content": "{\n \"text\": \"Codeine na-agba ah\\u1ee5 \\u1ecbnweta.\",\n \"words\": [\n {\n \"word\": \"codeine\",\n \"duration\": \"0.68\",\n \"codes\": [\n 165,\n 336,\n 1359,\n 661,\n 199,\n 379,\n 585,\n 1742,\n 210,\n 303,\n 388,\n 412,\n 1772,\n 794,\n 1607,\n 467,\n 622,\n 201,\n 575,\n 447,\n 319,\n 352,\n 234,\n 1797,\n 405,\n 1703,\n 1831,\n 1163,\n 1826,\n 1152,\n 563,\n 696,\n 1284,\n 157,\n 100,\n 402,\n 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681,\n 1071,\n 1732,\n 1392,\n 616,\n 706,\n 679,\n 510,\n 934,\n 37,\n 76,\n 1032,\n 1618,\n 944\n ]\n },\n {\n \"word\": \"ochichi\",\n \"duration\": \"0.44\",\n \"codes\": [\n 1234,\n 1267,\n 295,\n 1278,\n 891,\n 1652,\n 1142,\n 435,\n 356,\n 599,\n 70,\n 517,\n 1303,\n 788,\n 1314,\n 57,\n 1700,\n 1790,\n 432,\n 1495,\n 435,\n 823,\n 1583,\n 350,\n 290,\n 656,\n 70,\n 1074,\n 1104,\n 911,\n 1297,\n 1708,\n 1826\n ]\n }\n ]\n}" }, { "path": "default_speakers_local/igbo_male2.json", "content": "{\n \"text\": \"Any\\u1ecb na-eji nkw\\u1ee5 n'ihu na-eme fan aka\",\n \"words\": [\n {\n \"word\": \"anyi\",\n \"duration\": \"0.79\",\n \"codes\": [\n 165,\n 226,\n 672,\n 278,\n 1279,\n 924,\n 1648,\n 1079,\n 1010,\n 1321,\n 869,\n 964,\n 1118,\n 964,\n 691,\n 1033,\n 964,\n 762,\n 981,\n 772,\n 630,\n 967,\n 676,\n 676,\n 460,\n 567,\n 680,\n 301,\n 334,\n 981,\n 301,\n 334,\n 981,\n 316,\n 316,\n 316,\n 223,\n 1007,\n 571,\n 524,\n 402,\n 147,\n 367,\n 402,\n 303,\n 182,\n 1729,\n 510,\n 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t\\u00ed w\\u00f3\\u0323n \\u1e63e n\\u00edpa l\\u00edlo \\u1eb9\\u0300p\\u00e0, p\\u1eb9lu or\\u00eds\\u00ec\\u00edr\\u00eds\\u00ec\\u00ed \\u01f9kan\",\n \"words\": [\n {\n \"word\": \"kulikuli\",\n \"duration\": \"0.50\",\n \"codes\": [\n 156,\n 1777,\n 479,\n 1086,\n 243,\n 127,\n 170,\n 1275,\n 1470,\n 392,\n 278,\n 837,\n 1142,\n 284,\n 1411,\n 1742,\n 1280,\n 87,\n 898,\n 228,\n 67,\n 1499,\n 1568,\n 1035,\n 978,\n 157,\n 1078,\n 243,\n 1708,\n 170,\n 1498,\n 346,\n 344,\n 526,\n 1039,\n 316,\n 526\n ]\n },\n {\n \"word\": \"je\",\n \"duration\": \"0.28\",\n \"codes\": [\n 1570,\n 1290,\n 654,\n 328,\n 816,\n 270,\n 402,\n 271,\n 76,\n 43,\n 1259,\n 303,\n 371,\n 1077,\n 560,\n 1117,\n 1108,\n 1110,\n 1481,\n 691,\n 1825\n ]\n },\n {\n \"word\": \"nkan\",\n \"duration\": \"0.26\",\n \"codes\": [\n 1465,\n 1312,\n 538,\n 1807,\n 1152,\n 27,\n 20,\n 379,\n 1378,\n 1505,\n 84,\n 959,\n 756,\n 107,\n 949,\n 996,\n 1358,\n 1286,\n 755,\n 1686\n ]\n },\n {\n \"word\": \"ipanu\",\n 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\"word\": \"n\",\n \"duration\": \"0.12\",\n \"codes\": [\n 1002,\n 289,\n 47,\n 616,\n 1594,\n 852,\n 831,\n 458,\n 220\n ]\n },\n {\n \"word\": \"bale\",\n \"duration\": \"0.32\",\n \"codes\": [\n 953,\n 1426,\n 159,\n 1758,\n 474,\n 1347,\n 579,\n 699,\n 599,\n 1433,\n 483,\n 1142,\n 1088,\n 988,\n 906,\n 552,\n 128,\n 1648,\n 474,\n 1678,\n 668,\n 1060,\n 101,\n 1478\n ]\n },\n {\n \"word\": \"lowo\",\n \"duration\": \"0.22\",\n \"codes\": [\n 612,\n 326,\n 1661,\n 978,\n 88,\n 1620,\n 169,\n 811,\n 98,\n 363,\n 31,\n 425,\n 1531,\n 394,\n 1248,\n 809\n ]\n }\n ]\n}" }, { "path": "default_speakers_local/yoruba_male3.json", "content": "{\n \"text\": \"\\u00ccj\\u1ecdba t\\u00ed f\\u00ed \\u00f2fin d\\u00e9 t\\u00edta \\u1ecdt\\u00ed l\\u00edle.\",\n \"words\": [\n {\n \"word\": \"\\u00ccj\\u1ecdba\",\n \"duration\": \"0.67\",\n \"codes\": [\n 165,\n 1236,\n 1667,\n 933,\n 729,\n 1699,\n 1425,\n 1080,\n 1255,\n 458,\n 795,\n 1348,\n 334,\n 1458,\n 458,\n 566,\n 584,\n 187,\n 1774,\n 296,\n 123,\n 190,\n 1787,\n 1470,\n 558,\n 1392,\n 1693,\n 885,\n 1315,\n 760,\n 609,\n 357,\n 864,\n 575,\n 74,\n 798,\n 1401,\n 1380,\n 169,\n 1157,\n 871,\n 208,\n 622,\n 146,\n 1232,\n 107,\n 382,\n 801,\n 1707\n ]\n },\n {\n \"word\": \"t\\u00ed\",\n \"duration\": \"0.16\",\n \"codes\": [\n 459,\n 1475,\n 833,\n 1082,\n 1496,\n 1241,\n 1342,\n 211,\n 153,\n 1709,\n 1640,\n 468\n ]\n },\n {\n \"word\": \"f\\u00ed\",\n \"duration\": \"0.14\",\n \"codes\": [\n 1752,\n 1230,\n 854,\n 1420,\n 854,\n 1146,\n 1257,\n 388,\n 1686,\n 539,\n 289\n ]\n },\n {\n \"word\": \"\\u00f2fin\",\n \"duration\": \"0.26\",\n \"codes\": [\n 341,\n 1008,\n 1701,\n 359,\n 1696,\n 1250,\n 1226,\n 781,\n 1292,\n 1432,\n 989,\n 998,\n 236,\n 962,\n 1308,\n 749,\n 1462,\n 1460,\n 1039,\n 932\n ]\n },\n {\n \"word\": \"d\\u00e9\",\n \"duration\": \"0.16\",\n \"codes\": [\n 1020,\n 1808,\n 907,\n 276,\n 597,\n 1069,\n 217,\n 648,\n 1068,\n 468,\n 981,\n 1003\n ]\n },\n {\n \"word\": \"t\\u00edta\",\n \"duration\": \"0.46\",\n \"codes\": [\n 645,\n 1041,\n 605,\n 947,\n 1505,\n 162,\n 1820,\n 688,\n 101,\n 1764,\n 418,\n 885,\n 513,\n 1569,\n 1082,\n 446,\n 711,\n 294,\n 326,\n 1203,\n 1190,\n 524,\n 408,\n 222,\n 1490,\n 1162,\n 1486,\n 885,\n 247,\n 899,\n 513,\n 1187,\n 614,\n 424,\n 184\n ]\n },\n {\n \"word\": \"\\u1ecdt\\u00ed\",\n \"duration\": \"0.28\",\n \"codes\": [\n 979,\n 997,\n 1581,\n 620,\n 967,\n 460,\n 1430,\n 1731,\n 279,\n 499,\n 769,\n 517,\n 1077,\n 263,\n 1443,\n 397,\n 166,\n 1554,\n 440,\n 1009,\n 1427\n ]\n },\n {\n \"word\": \"l\\u00edle.\",\n \"duration\": \"0.28\",\n \"codes\": [\n 409,\n 1677,\n 599,\n 296,\n 629,\n 74,\n 129,\n 1740,\n 11,\n 1404,\n 920,\n 10,\n 269,\n 1604,\n 990,\n 1200,\n 1217,\n 1178,\n 293,\n 30,\n 36\n ]\n }\n ]\n}" }, { "path": "notebooks/Merge_datasets.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"mKb-4Hv4xNpF\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"8f45fbf9-5e31-4995-a18b-b5d2b9a9e9f5\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Collecting datasets\\n\",\n \" Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\\n\",\n \"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\\n\",\n \"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\\n\",\n \"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\\n\",\n \"Collecting dill<0.3.9,>=0.3.0 (from datasets)\\n\",\n \" Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\\n\",\n \"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\\n\",\n \"Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\\n\",\n \"Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.67.1)\\n\",\n \"Collecting xxhash (from datasets)\\n\",\n \" Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\\n\",\n \"Collecting multiprocess<0.70.17 (from datasets)\\n\",\n \" Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\\n\",\n \"Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\\n\",\n \" Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\\n\",\n \"Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.10)\\n\",\n \"Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.27.0)\\n\",\n \"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.2)\\n\",\n \"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\\n\",\n \"Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.4)\\n\",\n \"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.2)\\n\",\n \"Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\\n\",\n \"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.3.0)\\n\",\n \"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\\n\",\n \"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\\n\",\n \"Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (0.2.1)\\n\",\n \"Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.18.3)\\n\",\n \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\\n\",\n \"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.4.0)\\n\",\n \"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\\n\",\n \"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\\n\",\n \"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\\n\",\n \"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\\n\",\n \"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\\n\",\n \"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\\n\",\n \"Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m480.6/480.6 kB\\u001b[0m \\u001b[31m9.2 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m116.3/116.3 kB\\u001b[0m \\u001b[31m6.3 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m179.3/179.3 kB\\u001b[0m \\u001b[31m7.2 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m134.8/134.8 kB\\u001b[0m \\u001b[31m4.6 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m194.1/194.1 kB\\u001b[0m \\u001b[31m9.0 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\\n\",\n \" Attempting uninstall: fsspec\\n\",\n \" Found existing installation: fsspec 2024.10.0\\n\",\n \" Uninstalling fsspec-2024.10.0:\\n\",\n \" Successfully uninstalled fsspec-2024.10.0\\n\",\n \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n \"gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n \"\\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"pip install datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"-Oz-lmmExH_F\"\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import pandas as pd\\n\",\n \"import huggingface_hub\\n\",\n \"import datasets\\n\",\n \"from datasets import load_dataset, load_from_disk,concatenate_datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"all_df=[]\\n\",\n \"for df_path in os.listdir(\\\"/content/drive/MyDrive/Tokenized\\\"):\\n\",\n \" if ('.gsheet' not in df_path) and ((\\\"yt\\\" in df_path) or (\\\"mv\\\" in df_path)):\\n\",\n \" print(df_path)\\n\",\n \" all_df.append( pd.read_csv(f\\\"/content/drive/MyDrive/Tokenized/{df_path}\\\"))\\n\",\n \" #print(df_path)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"ACU3ZWjAfTsI\",\n \"outputId\": \"d57c0bd1-6161-4ef3-f722-480e1f40af05\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"tokenized_yt0.csv\\n\",\n \"tokenized_mv0.csv\\n\",\n \"tokenized_yt1.csv\\n\",\n \"tokenized_mv1.csv\\n\",\n \"tokenized_yt2.csv\\n\",\n \"tokenized_mv2.csv\\n\",\n \"tokenized_yt3.csv\\n\",\n \"tokenized_mv3.csv\\n\",\n \"tokenized_yt4.csv\\n\",\n \"tokenized_mv4.csv\\n\",\n \"tokenized_yt5.csv\\n\",\n \"tokenized_mv5.csv\\n\",\n \"tokenized_yt6.csv\\n\",\n \"tokenized_mv6.csv\\n\",\n \"tokenized_yt7.csv\\n\",\n \"tokenized_mv7.csv\\n\",\n \"tokenized_yt8.csv\\n\",\n \"tokenized_mv8.csv\\n\",\n \"tokenized_yt9.csv\\n\",\n \"tokenized_mv9.csv\\n\",\n \"tokenized_yt10.csv\\n\",\n \"tokenized_mv10.csv\\n\",\n \"tokenized_yt11.csv\\n\",\n \"tokenized_mv11.csv\\n\",\n \"tokenized_yt12.csv\\n\",\n \"tokenized_mv12.csv\\n\",\n \"tokenized_yt13.csv\\n\",\n \"tokenized_mv13.csv\\n\",\n \"tokenized_yt14.csv\\n\",\n \"tokenized_mv14.csv\\n\",\n \"tokenized_yt15.csv\\n\",\n \"tokenized_mv15.csv\\n\",\n \"tokenized_yt16.csv\\n\",\n \"tokenized_mv16.csv\\n\",\n \"tokenized_yt17.csv\\n\",\n \"tokenized_mv17.csv\\n\",\n \"tokenized_yt18.csv\\n\",\n \"tokenized_mv18.csv\\n\",\n \"tokenized_yt19.csv\\n\",\n \"tokenized_mv19.csv\\n\",\n \"tokenized_yt20.csv\\n\",\n \"tokenized_mv20.csv\\n\",\n \"tokenized_yt21.csv\\n\",\n \"tokenized_mv21.csv\\n\",\n \"tokenized_yt22.csv\\n\",\n \"tokenized_mv22.csv\\n\",\n \"tokenized_yt23.csv\\n\",\n \"tokenized_mv23.csv\\n\",\n \"tokenized_yt24.csv\\n\",\n \"tokenized_mv24.csv\\n\",\n \"tokenized_yt25.csv\\n\",\n \"tokenized_mv25.csv\\n\",\n \"tokenized_yt26.csv\\n\",\n \"tokenized_mv26.csv\\n\",\n \"tokenized_mv27.csv\\n\",\n \"tokenized_yt27.csv\\n\",\n \"tokenized_mv28.csv\\n\",\n \"tokenized_yt28.csv\\n\",\n \"tokenized_mv29.csv\\n\",\n \"tokenized_yt29.csv\\n\",\n \"tokenized_mv30.csv\\n\",\n \"tokenized_yt30.csv\\n\",\n \"tokenized_mv31.csv\\n\",\n \"tokenized_yt31.csv\\n\",\n \"tokenized_mv32.csv\\n\",\n \"tokenized_yt32.csv\\n\",\n \"tokenized_mv33.csv\\n\",\n \"tokenized_yt33.csv\\n\",\n \"tokenized_mv34.csv\\n\",\n \"tokenized_yt34.csv\\n\",\n \"tokenized_mv35.csv\\n\",\n \"tokenized_mv37.csv\\n\",\n \"tokenized_mv38.csv\\n\",\n \"tokenized_yt37.csv\\n\",\n \"tokenized_mv39.csv\\n\",\n \"tokenized_yt38.csv\\n\",\n \"tokenized_mv40.csv\\n\",\n \"tokenized_yt39.csv\\n\",\n \"tokenized_mv41.csv\\n\",\n \"tokenized_yt40.csv\\n\",\n \"tokenized_mv42.csv\\n\",\n \"tokenized_yt41.csv\\n\",\n \"tokenized_mv43.csv\\n\",\n \"tokenized_yt42.csv\\n\",\n \"tokenized_mv44.csv\\n\",\n \"tokenized_yt43.csv\\n\",\n \"tokenized_mv45.csv\\n\",\n \"tokenized_mv46.csv\\n\",\n \"tokenized_yt44.csv\\n\",\n 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\"execution_count\": null,\n \"metadata\": {\n \"id\": \"1QHW2w8cdupP\"\n },\n \"outputs\": [],\n \"source\": [\n \"import huggingface_hub\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"-Oz-lmmExH_F\"\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import pandas as pd\\n\",\n \"import huggingface_hub\\n\",\n \"import datasets\\n\",\n \"from datasets import load_dataset, load_from_disk,concatenate_datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"NfVKJ5xgdyc1\"\n },\n \"outputs\": [],\n \"source\": [\n \"huggingface_hub.login()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"llvhheVWjDwi\"\n },\n \"outputs\": [],\n \"source\": [\n \"from transformers import AutoModelForCausalLM, AutoTokenizer\\n\",\n \"checkpoint=\\\"saheedniyi/YarnGPT\\\"\\n\",\n \"#checkpoint=\\\"saheedniyi/public_extra2\\\"#device = 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],\n \"metadata\": {\n \"id\": \"9yebXiwvgZMa\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"def replace_text(txt):\\n\",\n \" txt=txt.replace(\\\"<|hausa|\\\\n\\\",\\\"<|hausa|>\\\\n\\\")\\n\",\n \" txt=txt.replace(\\\"<|igbo|\\\\n\\\",\\\"<|igbo|>\\\\n\\\")\\n\",\n \" txt=txt.replace(\\\"<|yoruba|\\\\n\\\",\\\"<|yoruba|>\\\\n\\\")#hausa\\\":\\\"<|hausa|\\\",\\n\",\n \" txt=txt.replace(\\\"\\\\n<|tts|>\\\",\\\"\\\")\\n\",\n \" return txt\"\n ],\n \"metadata\": {\n \"id\": \"kUvi1ItQg6HT\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"TgWv4W8Wiwd1\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_data_1=train_data_1[train_data_1[\\\"tts\\\"]!=\\\"An error occurred\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"train_data_1[\\\"tts\\\"]=train_data_1[\\\"tts\\\"].apply(replace_text)\"\n ],\n \"metadata\": {\n \"id\": \"CP7bVwRqh_qH\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"train_data_1.shape\"\n ],\n \"metadata\": {\n \"id\": \"YMk40ZsIrW44\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"train_data_1\"\n ],\n \"metadata\": {\n \"id\": \"RVD3f0frrcfi\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"train_data_1=train_data_1[train_data_1[\\\"length\\\"]<4000]\"\n ],\n \"metadata\": {\n \"id\": \"utJLIf1orhnE\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"train_data_1.to_csv(\\\"/content/drive/MyDrive/naij_tokenized/final_all_lang.csv\\\")\"\n ],\n \"metadata\": {\n \"id\": \"BOynFjQlruWJ\"\n },\n \"execution_count\": null,\n \"outputs\": []\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"machine_shape\": \"hm\",\n \"provenance\": []\n },\n 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MB\\u001b[0m \\u001b[31m87.2 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading tiktoken-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m1.2/1.2 MB\\u001b[0m \\u001b[31m52.5 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hBuilding wheels for collected packages: openai-whisper, encodec, pesq, unidic-lite\\n\",\n \" Building wheel for openai-whisper (pyproject.toml) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for openai-whisper: filename=openai_whisper-20240930-py3-none-any.whl size=803373 sha256=006ff9fec7048daea667dce09ad11d66d09d97d5e27939e2f27c96fd3223ab05\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/dd/4a/1f/d1c4bf3b9133c8168fe617ed979cab7b14fe381d059ffb9d83\\n\",\n \" Building wheel for encodec (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for encodec: filename=encodec-0.1.1-py3-none-any.whl size=45760 sha256=451b0ff87f503b1e3e80ee75873ae179f23b53b055ffcac6e5414d3bdf11dad3\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/fc/36/cb/81af8b985a5f5e0815312d5e52b41263237af07b977e6bcbf3\\n\"\n ]\n }\n ],\n \"source\": [\n \"pip install outetts uroman noisereduce mecab-python3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"HgJjekSOT8iX\"\n },\n \"outputs\": [],\n \"source\": [\n \"!pip install datasets triton snac wandb accelerate torchdata\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"m4uPM3IpnsEo\"\n },\n \"outputs\": [],\n \"source\": [\n \"from outetts.wav_tokenizer.decoder import WavTokenizer\\n\",\n \"from outetts.wav_tokenizer.encoder.utils import convert_audio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"543a-ZmC7xjE\"\n },\n \"outputs\": [],\n \"source\": [\n \"from google.colab import drive\\n\",\n \"drive.mount('/content/drive')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"EVyBedbQUM3F\"\n },\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import torchaudio\\n\",\n \"from snac import SNAC\\n\",\n \"from tqdm import tqdm\\n\",\n \"import huggingface_hub\\n\",\n \"import shutil\\n\",\n \"import soundfile as sf\\n\",\n \"from torch.utils.data import DataLoader, Dataset\\n\",\n \"from transformers import AdamW, get_linear_schedule_with_warmup\\n\",\n \"from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Z8LFkziTgFRf\"\n },\n \"outputs\": [],\n \"source\": [\n \"import torchaudio\\n\",\n \"import torch\\n\",\n \"import torchaudio.functional as F\\n\",\n \"import inflect\\n\",\n \"import re\\n\",\n \"import uroman as ur\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"-wARjdSEUdjy\"\n },\n \"outputs\": [],\n \"source\": [\n \"device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"IYyt-dhuWx9q\"\n },\n \"outputs\": [],\n \"source\": [\n \"config_path = \\\"/content/drive/MyDrive/audio_datasets/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\\\"\\n\",\n \"model_path = \\\"/content/drive/MyDrive/audio_datasets/wavtokenizer_large_speech_320_24k.ckpt\\\"#\\\"/content/wavtokenizer_medium_speech_320_24k_v2.ckpt\\\"\\n\",\n \"wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)\\n\",\n \"wavtokenizer = wavtokenizer.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"TrfYeoWNV6T9\"\n },\n \"outputs\": [],\n \"source\": [\n \"class CTCForcedAlignment:\\n\",\n \"\\n\",\n \" def __init__(self, device: str = None):\\n\",\n \" self.device = torch.device(device if device is not None else \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n \" bundle = torchaudio.pipelines.MMS_FA\\n\",\n \" self.sample_rate = bundle.sample_rate\\n\",\n \" self.model = bundle.get_model(with_star=False).to(self.device)\\n\",\n \" self.LABELS = bundle.get_labels(star=None)\\n\",\n \" self.DICTIONARY = bundle.get_dict(star=None)\\n\",\n \" self.lec = inflect.engine()\\n\",\n \" self.uroman = ur.Uroman()\\n\",\n \" #self.wakati = MeCab.Tagger(\\\"-Owakati\\\")\\n\",\n \" #self.wakati_use = [\\\"ja\\\", \\\"zh\\\", \\\"ko\\\"]\\n\",\n \" #self.languages = languages\\n\",\n \"\\n\",\n \" def process_text(self, text: str):\\n\",\n \" #if language not in self.languages:\\n\",\n \" # raise ValueError(f\\\"Language {language} not supported, supported languages are {self.languages}\\\")\\n\",\n \" text = self.uroman.romanize_string(text)\\n\",\n \" text = re.sub(r'\\\\d+(\\\\.\\\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\\n\",\n \" text = re.sub(r'[-_/,\\\\.\\\\\\\\]', ' ', text)\\n\",\n \" text = re.sub(r'[^a-z\\\\s]', '', text)\\n\",\n \" text = re.sub(r'\\\\s+', ' ', text).strip()\\n\",\n \" return text.split()\\n\",\n \"\\n\",\n \" def _unflatten(self, list_, lengths):\\n\",\n \" assert len(list_) == sum(lengths)\\n\",\n \" i = 0\\n\",\n \" ret = []\\n\",\n \" for l in lengths:\\n\",\n \" ret.append(list_[i : i + l])\\n\",\n \" i += l\\n\",\n \" return ret\\n\",\n \"\\n\",\n \" def get_word(self, waveform, spans, num_frames, transcript):\\n\",\n \" ratio = waveform.size(1) / num_frames\\n\",\n \" x0 = int(ratio * spans[0].start)\\n\",\n \" x1 = int(ratio * spans[-1].end)\\n\",\n \" return {\\\"x0\\\": x0, \\\"x1\\\": x1, \\\"word\\\": transcript}\\n\",\n \"\\n\",\n \" def _extract_world_level(self, aligned_tokens, alignment_scores, transcript):\\n\",\n \" token_spans = F.merge_tokens(aligned_tokens, alignment_scores)\\n\",\n \" word_spans = self._unflatten(token_spans, [len(word) for word in transcript])\\n\",\n \" return word_spans\\n\",\n \"\\n\",\n \" def _align(self, emission, tokens):\\n\",\n \" targets = torch.tensor([tokens], dtype=torch.int32, device=torch.device(\\\"cpu\\\"))\\n\",\n \" alignments, scores = F.forced_align(emission.cpu(), targets, blank=0)\\n\",\n \" alignments, scores = alignments[0], scores[0]\\n\",\n \" scores = scores.exp()\\n\",\n \" return alignments, scores\\n\",\n \"\\n\",\n \" def align(self, waveform,sr, transcript):\\n\",\n \" #waveform, sr = torchaudio.load(audio)\\n\",\n \" #waveform = torch.tensor(waveform)\\n\",\n \" all_codes=quantize_wavtokenizer_ctc(waveform,sampling_rate=sr)\\n\",\n \" if waveform.shape[0] > 1:\\n\",\n \" waveform = waveform.mean(dim=0, keepdim=True)\\n\",\n \" waveform = waveform.float()\\n\",\n \" #print(waveform.shape)\\n\",\n \" #print(sr)\\n\",\n \" waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=self.sample_rate)\\n\",\n \" transcript = self.process_text(transcript)\\n\",\n \"\\n\",\n \" with torch.inference_mode():\\n\",\n \" emission, _ = self.model(waveform.to(self.device))\\n\",\n \"\\n\",\n \" tokenized_transcript = [self.DICTIONARY[c] for word in transcript for c in word]\\n\",\n \" alignments, scores = self._align(emission, tokenized_transcript)\\n\",\n \" word_spans = self._extract_world_level(alignments, scores, transcript)\\n\",\n \" num_frames = emission.size(1)\\n\",\n \"\\n\",\n \" outputs = [\\n\",\n \" self.get_word(waveform, word_spans[i], num_frames, transcript[i])\\n\",\n \" for i in range(len(word_spans))\\n\",\n \" ]\\n\",\n \" #codes=quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000):\\n\",\n \" #audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \"\\n\",\n \" outputs[0][\\\"x0\\\"] = 0\\n\",\n \" #print(waveform.shape)\\n\",\n \" #print(self.sample_rate)\\n\",\n \" for i in range(len(outputs)):\\n\",\n \" output = outputs[i]\\n\",\n \" x0 = output[\\\"x0\\\"]\\n\",\n \"\\n\",\n \" if i == len(outputs) - 1:\\n\",\n \" x1 = output[\\\"x1\\\"]\\n\",\n \" else:\\n\",\n \" x1 = outputs[i + 1][\\\"x0\\\"]\\n\",\n \" outputs[i][\\\"audio\\\"] = waveform[:, x0:x1]\\n\",\n \" outputs[i][\\\"duration\\\"]=len(outputs[i][\\\"audio\\\"][0])/self.sample_rate\\n\",\n \" outputs[i][\\\"codes\\\"]=all_codes[int(x0*75/self.sample_rate) : int(x1*75/self.sample_rate)]#quantize_wavtokenizer_ctc(outputs[i][\\\"audio\\\"],sampling_rate=16000, quantizer=wavtokenizer)\\n\",\n \" #convert waveform to codes\\n\",\n \" #duration Add audio\\n\",\n \" return outputs\\n\",\n \"\\n\",\n \" def free(self):\\n\",\n \" del self.model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"CouG9BMIV6-K\"\n },\n \"outputs\": [],\n \"source\": [\n \"ctc = CTCForcedAlignment(\\\"cuda\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"68rBtr5GUcF2\"\n },\n \"outputs\": [],\n \"source\": [\n \"ctc.DICTIONARY\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"275g7SweCKAe\"\n },\n \"outputs\": [],\n \"source\": [\n \"def resample(audio: np.ndarray, sr: int, target_sr: int):\\n\",\n \"\\n\",\n \" audio = audio.to(dtype=torch.float32)\\n\",\n \" #.clone().detach()\\n\",\n \" audio = audio.unsqueeze(0)\\n\",\n \" # 1 as last arg corresponds to mono audio\\n\",\n \" resampled = convert_audio(audio, sr, target_sr, 1)\\n\",\n \" return resampled.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"N85dYwCmWZG8\"\n },\n \"outputs\": [],\n \"source\": [\n \"def quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000, quantizer=wavtokenizer):\\n\",\n \" #audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \" audio = resample(audio_data, sampling_rate, 24000).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio=audio.squeeze(0)\\n\",\n \" _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\\n\",\n \" codes = codes.squeeze(1).to(device)#+last_text_token\\n\",\n \"\\n\",\n \" return codes[0].tolist()#+last_text_token\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"QgGSndp8AoVW\"\n },\n \"outputs\": [],\n \"source\": [\n \"def resample(audio: np.ndarray, sr: int, target_sr: int):\\n\",\n \"\\n\",\n \" audio =audio.to(dtype=torch.float32)\\n\",\n \" #.clone().detach()\\n\",\n \" audio = audio.unsqueeze(0)\\n\",\n \" # 1 as last arg corresponds to mono audio\\n\",\n \" resampled = convert_audio(audio, sr, target_sr, 1)\\n\",\n \" return resampled.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"txxV2uboCYih\"\n },\n \"outputs\": [],\n \"source\": [\n \"def quantize_wavtokenizer(row, quantizer=wavtokenizer):\\n\",\n \" audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \" audio = resample(audio_data, sample_rate, 24000).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" #print(audio.shape)\\n\",\n \" #print(audio.dim())\\n\",\n \" _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\\n\",\n \" codes = codes.squeeze(1).to(device)#+last_text_token\\n\",\n \"\\n\",\n \" return codes[0].tolist()#+last_text_token\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"JDfRH6HUIGiX\"\n },\n \"outputs\": [],\n \"source\": [\n \"def decode_tokenizer(discrete_code):\\n\",\n \" #discrete code is a list\\n\",\n \" discrete_code=torch.tensor([discrete_code]).to(device)-last_text_token\\n\",\n \" features = wavtokenizer.codes_to_features(discrete_code).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\\n\",\n \" return audio_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"0U_45AQey40V\"\n },\n \"outputs\": [],\n \"source\": [\n \"def decode_tokenizer(discrete_code):\\n\",\n \" #discrete code is a list\\n\",\n \" discrete_code=torch.tensor([[discrete_code]]).to(device)#-last_text_token\\n\",\n \" features = wavtokenizer.codes_to_features(discrete_code).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\\n\",\n \" return audio_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ij19rZw-fEQ0\"\n },\n \"outputs\": [],\n \"source\": [\n \"class PromptProcessor():\\n\",\n \" def __init__(self,lang):\\n\",\n \" self.lang=lang\\n\",\n \" self.bos = \\\"<|im_start|>\\\"\\n\",\n \" self.eos = \\\"<|im_end|>\\\"\\n\",\n \" self.tts_prompt = \\\"{bos}\\\\n{tts}\\\\n{text_start}{words}{text_end}\\\\n{lang}\\\\n{audio_start}\\\\n\\\"\\n\",\n \" self.stt_prompt = \\\"{bos}\\\\n{stt}\\\\n{audio_start}{codes}{audio_end}\\\\n{lang}\\\\n{text_start}\\\\n\\\"\\n\",\n \" self.special_tokens = {\\n\",\n \" \\\"audio_code\\\": \\\"<|{}|>\\\",\\n\",\n \" \\\"tts\\\":\\\"<|tts|>\\\",\\n\",\n \" \\\"stt\\\":\\\"<|stt|>\\\",\\n\",\n \" \\\"text_start\\\": \\\"<|text_start|>\\\",\\n\",\n \" \\\"text_end\\\": \\\"<|text_end|>\\\",\\n\",\n \" \\\"audio_start\\\": \\\"<|audio_start|>\\\",\\n\",\n \" \\\"audio_end\\\": \\\"<|audio_end|>\\\",\\n\",\n \" \\\"word_start\\\": \\\"<|word_start|>\\\",\\n\",\n \" \\\"word_end\\\": \\\"<|word_end|>\\\",\\n\",\n \" \\\"time\\\": \\\"<|t_{:.2f}|>\\\",\\n\",\n \" \\\"code_start\\\": \\\"<|code_start|>\\\",\\n\",\n \" \\\"code_end\\\": \\\"<|code_end|>\\\",\\n\",\n \" \\\"text_sep\\\": \\\"<|text_sep|>\\\",\\n\",\n \" \\\"hausa\\\":\\\"<|hausa|\\\">,\\n\",\n \" \\\"igbo\\\":\\\"<|igbo|\\\">,\\n\",\n \" \\\"yoruba\\\":\\\"<|yoruba|>\\\",\\n\",\n \"\\n\",\n \" }\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \"\\n\",\n \" def create_results_prompts(self,words):\\n\",\n \" prompt_audio= []\\n\",\n \" prompt_text=[]\\n\",\n \" all_tokens=[]\\n\",\n \" for i in words:\\n\",\n \" word = i[\\\"word\\\"]\\n\",\n \" duration = self.special_tokens[\\\"time\\\"].format(i[\\\"duration\\\"])\\n\",\n \" tokens = \\\"\\\".join([self.special_tokens[\\\"audio_code\\\"].format(c) for c in i[\\\"codes\\\"]])\\n\",\n \" all_tokens.append(tokens)\\n\",\n \" prompt_audio.append(f'{word}{duration}{self.special_tokens[\\\"code_start\\\"]}{tokens}{self.special_tokens[\\\"code_end\\\"]}')\\n\",\n \" prompt_text.append(f'{tokens}{duration}{self.special_tokens[\\\"word_start\\\"]}{word}{self.special_tokens[\\\"word_end\\\"]}')\\n\",\n \" return \\\"\\\".join(all_tokens),\\\"\\\\n\\\".join(prompt_audio),\\\"\\\\n\\\".join(prompt_text)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" def get_prompt(self, row):\\n\",\n \" try:\\n\",\n \" audio=torch.from_numpy(row[\\\"audio\\\"][\\\"array\\\"]).unsqueeze(0)#torch.tensor([row[\\\"audio\\\"][\\\"array\\\"]])\\n\",\n \" #print(audio)\\n\",\n \" sample_rate=row[\\\"audio\\\"][\\\"sampling_rate\\\"]\\n\",\n \" if row[\\\"text\\\"]:\\n\",\n \" transcript=row[\\\"text\\\"]\\n\",\n \" else:\\n\",\n \" transcript=row[\\\"transcript\\\"]\\n\",\n \" input_words = ctc.process_text(transcript)\\n\",\n \" words= ctc.align(audio,sample_rate,transcript)\\n\",\n \" #print(words)\\n\",\n \" inputs_words_strings = f\\\"{self.special_tokens['text_sep']}\\\".join([i.strip() for i in input_words])\\n\",\n \" #self.text_prompt = \\\"{bos}\\\\n{text_start}{words}{text_end}\\\\n{audio_start}\\\\n\\\"\\n\",\n \" prompt_tts= self.tts_prompt.format(\\n\",\n \" bos=self.bos,\\n\",\n \" text_start=self.special_tokens['text_start'],\\n\",\n \" tts=self.special_tokens['tts'],\\n\",\n \" words=inputs_words_strings,\\n\",\n \" lang=self.special_tokens[self.lang],\\n\",\n \" text_end=self.special_tokens['text_end'],\\n\",\n \" audio_start=self.special_tokens['audio_start']\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \" all_codes, tts_extra, stt_extra=self.create_results_prompts(words)\\n\",\n \" prompt_stt=self.stt_prompt.format(\\n\",\n \" bos=self.bos,\\n\",\n \" audio_start=self.special_tokens['audio_start'],\\n\",\n \" stt=self.special_tokens['stt'],\\n\",\n \" codes=all_codes,\\n\",\n \" lang=self.special_tokens[self.lang],\\n\",\n \"\\n\",\n \" audio_end=self.special_tokens['audio_end'],\\n\",\n \" text_start=self.special_tokens['text_start']\\n\",\n \" )\\n\",\n \" prompt_stt+=stt_extra+f\\\"\\\\n{self.special_tokens['text_end']}\\\\n{self.eos}\\\\n\\\"\\n\",\n \" prompt_tts+=tts_extra+f\\\"\\\\n{self.special_tokens['audio_end']}\\\\n{self.eos}\\\\n\\\"\\n\",\n \"\\n\",\n \" return {\\\"stt\\\":prompt_stt,\\\"tts\\\":prompt_tts}\\n\",\n \" except Exception as e:\\n\",\n \" #print(e)\\n\",\n \" return {\\\"stt\\\":\\\"An error occurred\\\",\\\"tts\\\":\\\"An error occurred\\\"}#,\\\"An error occured\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ctohbEGTfZYq\"\n },\n \"outputs\": [],\n \"source\": [\n \"ps=PromptProcessor(\\\"yoruba\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 17,\n \"referenced_widgets\": [\n \"9f80b9ce82aa4c2bb3e6da8edb4887ef\",\n \"4d77ee1fa6ed43efa05683b12cf26239\"\n ]\n },\n \"id\": \"Q7R28b7gd-9f\",\n \"outputId\": \"0c44d8ba-582f-42ca-f859-acb9a52a5729\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"9f80b9ce82aa4c2bb3e6da8edb4887ef\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"VBox(children=(HTML(value='
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],\n \"application/vnd.google.colaboratory.intrinsic+json\": {\n \"type\": \"string\"\n }\n },\n \"metadata\": {},\n \"execution_count\": 53\n }\n ],\n \"source\": [\n \"ps.get_prompt(k)[\\\"tts\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"43YFGwbEbWkN\"\n },\n \"outputs\": [],\n \"source\": [\n \"data_yoruba = data_yoruba.cast_column(\\\"audio\\\", Audio(sampling_rate=24000))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"6aDesKzcQZQn\",\n \"outputId\": \"455497c1-814c-40f8-a6b4-c9812880aa96\"\n },\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"Dataset({\\n\",\n \" features: ['audio', 'text'],\\n\",\n \" num_rows: 15188\\n\",\n \"})\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 56\n }\n ],\n \"source\": [\n \"data_yoruba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"bMOmeJx5IkAn\"\n },\n \"outputs\": [],\n \"source\": [\n \"start=0\\n\",\n \"end=len(data_yoruba)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"--KPdDtTvVrN\",\n \"outputId\": \"b0a99ed0-cfb7-416b-ed7f-7f52fa778517\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"15188\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(end)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ZtcDBjbQh39V\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 806,\n \"referenced_widgets\": [\n \"e0b88a0e362c4a6a90007d6dbb7898f7\",\n 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/root/.cache/pip/wheels/c5/4e/2c/251524370c0fdd659e99639a0fbd0ca5a782c3aafcd456b28d\\n\",\n \" Building wheel for unidic-lite (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for unidic-lite: filename=unidic_lite-1.0.8-py3-none-any.whl size=47658818 sha256=446f876b7d53ed4cd89bdd8a3e6fca77757adffa2ecb331c6e1291119d666e55\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/89/e8/68/f9ac36b8cc6c8b3c96888cd57434abed96595d444f42243853\\n\",\n \"Successfully built openai-whisper encodec pesq unidic-lite\\n\",\n \"Installing collected packages: unidic-lite, pesq, mecab-python3, uroman, triton, tensorboardX, loguru, lightning-utilities, jsonargparse, tiktoken, sounddevice, resampy, torchmetrics, openai-whisper, torchcrepe, encodec, pytorch-lightning, outetts\\n\",\n \"Successfully installed encodec-0.1.1 jsonargparse-4.35.0 lightning-utilities-0.11.9 loguru-0.7.3 mecab-python3-1.0.10 openai-whisper-20240930 outetts-0.2.3 pesq-0.0.4 pytorch-lightning-2.5.0.post0 resampy-0.4.3 sounddevice-0.5.1 tensorboardX-2.6.2.2 tiktoken-0.8.0 torchcrepe-0.0.23 torchmetrics-1.6.1 triton-3.1.0 unidic-lite-1.0.8 uroman-1.3.1.1\\n\"\n ]\n }\n ],\n \"source\": [\n \"pip install outetts uroman\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"HgJjekSOT8iX\",\n \"outputId\": \"67d1139a-a4d5-4afc-d05f-93fedabe0963\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting datasets\\n\",\n \" Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\\n\",\n \"Requirement already satisfied: triton in /usr/local/lib/python3.10/dist-packages (3.1.0)\\n\",\n \"Collecting snac\\n\",\n \" Downloading snac-1.2.1-py3-none-any.whl.metadata (3.5 kB)\\n\",\n \"Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.19.1)\\n\",\n \"Requirement already satisfied: accelerate in 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gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb) (5.0.1)\\n\",\n \"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->snac) (3.0.2)\\n\",\n \"Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m480.6/480.6 kB\\u001b[0m \\u001b[31m31.1 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading snac-1.2.1-py3-none-any.whl (8.4 kB)\\n\",\n \"Downloading torchdata-0.10.1-py3-none-any.whl (57 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m57.5/57.5 kB\\u001b[0m \\u001b[31m4.7 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m116.3/116.3 kB\\u001b[0m \\u001b[31m8.4 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m179.3/179.3 kB\\u001b[0m \\u001b[31m14.5 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m134.8/134.8 kB\\u001b[0m \\u001b[31m11.2 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m194.1/194.1 kB\\u001b[0m \\u001b[31m15.6 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, torchdata, snac, datasets\\n\",\n \" Attempting uninstall: fsspec\\n\",\n \" Found existing installation: fsspec 2024.10.0\\n\",\n \" Uninstalling fsspec-2024.10.0:\\n\",\n \" Successfully uninstalled fsspec-2024.10.0\\n\",\n \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n \"gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n \"\\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 snac-1.2.1 torchdata-0.10.1 xxhash-3.5.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install datasets triton snac wandb accelerate torchdata\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"m4uPM3IpnsEo\",\n \"outputId\": \"0e2fed8e-9448-426f-e5ad-c3403faf68bd\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[32m2024-12-31 12:17:21.441\\u001b[0m | \\u001b[31m\\u001b[1mERROR \\u001b[0m | \\u001b[36moutetts.version.v1.interface\\u001b[0m:\\u001b[36m\\u001b[0m:\\u001b[36m21\\u001b[0m - \\u001b[31m\\u001b[1mPortAudio library not found\\u001b[0m\\n\",\n \"\\u001b[32m2024-12-31 12:17:21.443\\u001b[0m | \\u001b[33m\\u001b[1mWARNING \\u001b[0m | \\u001b[36moutetts.version.v1.interface\\u001b[0m:\\u001b[36m\\u001b[0m:\\u001b[36m22\\u001b[0m - \\u001b[33m\\u001b[1mFailed to import sounddevice. Audio playback is disabled.\\u001b[0m\\n\"\n ]\n }\n ],\n \"source\": [\n \"from outetts.wav_tokenizer.decoder import WavTokenizer\\n\",\n \"from outetts.wav_tokenizer.encoder.utils import convert_audio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"543a-ZmC7xjE\",\n \"outputId\": \"3572c26c-f122-4fa3-aaa8-0cd93aeb13c1\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Mounted at /content/drive\\n\"\n ]\n }\n ],\n \"source\": [\n \"from google.colab import drive\\n\",\n \"drive.mount('/content/drive')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"EVyBedbQUM3F\"\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"import os\\n\",\n \"import torch\\n\",\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import torchaudio\\n\",\n \"#from snac import SNAC\\n\",\n \"from tqdm import tqdm\\n\",\n \"import huggingface_hub\\n\",\n \"import shutil\\n\",\n \"import soundfile as sf\\n\",\n \"from torch.utils.data import DataLoader, Dataset\\n\",\n \"from transformers import AdamW, get_linear_schedule_with_warmup, DataCollatorWithPadding\\n\",\n \"from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets,Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Z8LFkziTgFRf\"\n },\n \"outputs\": [],\n \"source\": [\n \"import torchaudio\\n\",\n \"import torch\\n\",\n \"import torchaudio.functional as F\\n\",\n \"import inflect\\n\",\n \"import re\\n\",\n \"import uroman as ur\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 17,\n \"referenced_widgets\": [\n \"c8b57e894816423d8001c6431f4fcb8f\",\n \"06360960a0c44095b4438561b56250c6\"\n ]\n },\n \"id\": \"DN19SQCOUc6m\",\n \"outputId\": \"38921b10-5798-4cd0-ee64-746f9cbba82c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"c8b57e894816423d8001c6431f4fcb8f\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"VBox(children=(HTML(value='
\\\",\\n\",\n \" \\\"<|im_end|>\\\",\\n\",\n \" \\\"<|text_start|>\\\",\\n\",\n \" \\\"<|text_end|>\\\",\\n\",\n \" \\\"<|audio_start|>\\\",\\n\",\n \" \\\"<|audio_end|>\\\",\\n\",\n \" \\\"<|code_start|>\\\",\\n\",\n \" \\\"<|code_end|>\\\",\\n\",\n \" \\\"<|text_sep|>\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"2jHr-11YmoId\",\n \"outputId\": \"7c511577-3533-4201-a67b-70102ca53c3b\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2024\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"audio_tokens=[f\\\"<|{i}|>\\\" for i in range(0,2024)]\\n\",\n \"tokenizer.add_tokens(audio_tokens)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"xZKFh8sdmqWB\",\n \"outputId\": \"eec66d52-e0dd-4e33-8aef-8c2bcc91db74\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1000\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"time_tokens=[f\\\"<|t_{round(i,2)}|>\\\" for i in np.arange(0,10,0.01)]#time_tokens\\n\",\n \"tokenizer.add_tokens(time_tokens)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"OJk2i5urKIec\",\n \"outputId\": \"779099fa-c0aa-4dee-8009-f11d953116b6\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tokenizer.eos_token_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"6BPy5GpEKGP_\"\n },\n \"outputs\": [],\n \"source\": [\n \"tokenizer.pad_token_id=0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"N90blgKsHJo6\",\n \"outputId\": \"07cbd6bb-4209-4751-875b-2214f688c62d\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"52183\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(tokenizer)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"AyP1GBHDoO4v\"\n },\n \"outputs\": [],\n \"source\": [\n \"total_tokens=49152+4096\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"tMpZOv8gVya2\",\n \"outputId\": \"3b26d2dc-051a-411b-dfd6-96a45fd5bb5f\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The new embeddings will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Embedding(53248, 960)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.resize_token_embeddings(total_tokens)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"1yjGTRLMWI26\"\n },\n \"outputs\": [],\n \"source\": [\n \"model=torch.compile(model.to(device))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"QOxVQVeZWL1d\",\n \"outputId\": \"775ef716-27b0-4e4b-a0a9-b9547eacd55c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"731.510784\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.get_memory_footprint()/ 1e6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"Zqe1ZczmWP1b\",\n \"outputId\": \"f7c64a79-d17a-4694-bc8a-502f9bbbb6f7\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"365753280\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.num_parameters()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"IYyt-dhuWx9q\",\n \"outputId\": \"08a27722-2365-44b2-ec22-62914a7b8f1e\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\\n\",\n \" WeightNorm.apply(module, name, dim)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"making attention of type 'vanilla' with 768 in_channels\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.10/dist-packages/outetts/wav_tokenizer/decoder/pretrained.py:101: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\\n\",\n \" state_dict_raw = torch.load(model_path, map_location=\\\"cpu\\\")['state_dict']\\n\"\n ]\n }\n ],\n \"source\": [\n \"config_path = \\\"/content/drive/MyDrive/audio_datasets/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\\\"\\n\",\n \"model_path = \\\"/content/drive/MyDrive/audio_datasets/wavtokenizer_large_speech_320_24k.ckpt\\\"#\\\"/content/wavtokenizer_medium_speech_320_24k_v2.ckpt\\\"\\n\",\n \"wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)\\n\",\n \"wavtokenizer = wavtokenizer.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"275g7SweCKAe\"\n },\n \"outputs\": [],\n \"source\": [\n \"def resample(audio: np.ndarray, sr: int, target_sr: int):\\n\",\n \" audio = audio.to(dtype=torch.float32)\\n\",\n \" #.clone().detach()\\n\",\n \" audio = audio.unsqueeze(0)\\n\",\n \" # 1 as last arg corresponds to mono audio\\n\",\n \" resampled = convert_audio(audio, sr, target_sr, 1)\\n\",\n \" return resampled.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"N85dYwCmWZG8\"\n },\n \"outputs\": [],\n \"source\": [\n \"def quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000, quantizer=wavtokenizer):\\n\",\n \" #audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \" audio = resample(audio_data, sampling_rate, 24000).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio=audio.squeeze(0)\\n\",\n \" _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\\n\",\n \" codes = codes.squeeze(1).to(device)#+last_text_token\\n\",\n \"\\n\",\n \" return codes[0].tolist()#+last_text_token\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"txxV2uboCYih\"\n },\n \"outputs\": [],\n \"source\": [\n \"def quantize_wavtokenizer(row, quantizer=wavtokenizer):\\n\",\n \" audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \" audio = resample(audio_data, sample_rate, 24000).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" #print(audio.shape)\\n\",\n \" #print(audio.dim())\\n\",\n \" _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\\n\",\n \" codes = codes.squeeze(1).to(device)#+last_text_token\\n\",\n \"\\n\",\n \" return codes[0].tolist()#+last_text_token\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"0U_45AQey40V\"\n },\n \"outputs\": [],\n \"source\": [\n \"def decode_tokenizer(discrete_code):\\n\",\n \" #discrete code is a list\\n\",\n \" discrete_code=torch.tensor([[discrete_code]]).to(device)#-last_text_token\\n\",\n \" features = wavtokenizer.codes_to_features(discrete_code).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\\n\",\n \" return audio_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"gRdr07gcLN7H\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_data=pd.read_csv(\\\"/content/drive/MyDrive/Tokenized2/all_data.csv\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 461\n },\n \"id\": \"t9rddLyYLgnh\",\n \"outputId\": \"a73f13da-9bd7-48fe-8039-c06355a8d983\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.google.colaboratory.intrinsic+json\": {\n \"type\": \"dataframe\",\n \"variable_name\": \"train_data\"\n },\n \"text/html\": [\n \"\\n\",\n \"
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\"source\": [\n \"train_data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"1oYlD-iTIFXw\",\n \"outputId\": \"93a3d98b-5ba4-44f2-8069-ae127a2bc08c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(642150, 3)\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_data.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"mR8OImavy1Z5\",\n \"outputId\": \"4664f2a2-c722-43e4-c7d3-e6fff69513fd\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3999\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_data[\\\"length\\\"].max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"AFTfQ4RHIyw8\",\n \"outputId\": \"3fae2b3f-1920-4230-ecef-813e82382b30\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(642150, 3)\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_data.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"nDWFPH4YI4q-\"\n },\n \"outputs\": [],\n \"source\": [\n \"#train_data=train_data.reset_index(drop=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"t0QWUWIlI-U9\"\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"from datasets import Dataset\\n\",\n \"train_dataset=Dataset.from_pandas(train_data[[\\\"0\\\"]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"QepGS-a8qSJL\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_dataset=train_dataset.shuffle()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Eoxv09xaGsM_\"\n },\n \"outputs\": [],\n \"source\": [\n \"from torch.utils.data import DataLoader, Dataset\\n\",\n \"class YarnDataset(Dataset):\\n\",\n \" def __init__(self,dataset):\\n\",\n \" self.ds = dataset\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \" def __len__(self):\\n\",\n \" return len(self.ds)\\n\",\n \"\\n\",\n \"\\n\",\n \" def __getitem__(self, idx):\\n\",\n \" prompt=self.ds[idx][\\\"0\\\"]\\n\",\n \" #print(prompt)\\n\",\n \" return tokenizer(prompt,)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"PdiQt7_Ctlbb\"\n },\n \"outputs\": [],\n \"source\": [\n \"yarn_dataset = YarnDataset(train_dataset)#train_dataset.select(rang(0600000))#CustomDataSetAfriSpeech(data)#\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"QJBKk_oXQ3HP\"\n },\n \"outputs\": [],\n \"source\": [\n \"batch_size=4\\n\",\n \"learning_rate=1e-3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"qPkuxPZIO1dP\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Initialize data collator\\n\",\n \"data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\\n\",\n \"\\n\",\n \"# Create DataLoader with collate_fn using data collator\\n\",\n \"dataloader = DataLoader(\\n\",\n \" yarn_dataset,\\n\",\n \" batch_size=batch_size,\\n\",\n \" collate_fn=data_collator,shuffle=True # Automatically handles padding\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"XHZZVQBQRaXi\",\n \"outputId\": \"ab1af510-f5f9-4944-e6b7-8e23da2c907a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"642150\"\n ]\n 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\"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(dataloader)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"1Ez7HQd7GFEA\"\n },\n \"outputs\": [],\n \"source\": [\n \"from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts\\n\",\n \"from torch.optim.lr_scheduler import LambdaLR\\n\",\n \"from transformers import get_linear_schedule_with_warmup,get_cosine_schedule_with_warmup,get_constant_schedule_with_warmup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"kBWFROqbzO3h\"\n },\n \"outputs\": [],\n \"source\": [\n \"def get_lr_lambda(step):\\n\",\n \" if step < lr_warmup_steps:\\n\",\n \" # Linear warmup\\n\",\n \" return step / lr_warmup_steps\\n\",\n \" elif step >=(num_decay_start):\\n\",\n \" return 1-(step-num_decay_start)/(num_training_steps-num_decay_start)\\n\",\n \" else:\\n\",\n \" # Constant learning rate\\n\",\n \" return 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"18ObtWm1Ryr3\"\n },\n \"outputs\": [],\n \"source\": [\n \"#0.2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"AVI3iLgUbYnW\",\n \"outputId\": \"432aef8c-f0c8-4bbd-bd43-2bbaf235e5df\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OptimizedModule(\\n\",\n \" (_orig_mod): LlamaForCausalLM(\\n\",\n \" (model): LlamaModel(\\n\",\n \" (embed_tokens): Embedding(53248, 960)\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0-31): 32 x LlamaDecoderLayer(\\n\",\n \" (self_attn): LlamaSdpaAttention(\\n\",\n \" (q_proj): Linear(in_features=960, out_features=960, bias=False)\\n\",\n \" (k_proj): Linear(in_features=960, out_features=320, bias=False)\\n\",\n \" (v_proj): Linear(in_features=960, out_features=320, bias=False)\\n\",\n \" (o_proj): Linear(in_features=960, out_features=960, bias=False)\\n\",\n \" (rotary_emb): LlamaRotaryEmbedding()\\n\",\n \" )\\n\",\n \" (mlp): LlamaMLP(\\n\",\n \" (gate_proj): Linear(in_features=960, out_features=2560, bias=False)\\n\",\n \" (up_proj): Linear(in_features=960, out_features=2560, bias=False)\\n\",\n \" (down_proj): Linear(in_features=2560, out_features=960, bias=False)\\n\",\n \" (act_fn): SiLU()\\n\",\n \" )\\n\",\n \" (input_layernorm): LlamaRMSNorm((960,), eps=1e-05)\\n\",\n \" (post_attention_layernorm): LlamaRMSNorm((960,), eps=1e-05)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (norm): LlamaRMSNorm((960,), eps=1e-05)\\n\",\n \" (rotary_emb): LlamaRotaryEmbedding()\\n\",\n \" )\\n\",\n \" (lm_head): Linear(in_features=960, out_features=53248, bias=False)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n \"execution_count\": 100,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"num_epochs=5\\n\",\n \"optimizer = AdamW(model.parameters(), lr=learning_rate, betas=(0.9, 0.95),weight_decay=0.01)\\n\",\n \"lr_warmup_steps=50\\n\",\n \"\\n\",\n \"num_training_steps=1255*num_epochs\\n\",\n \"num_decay_start=50#num_training_steps#-20\\n\",\n \"#scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=T_0, T_mult=T_mult, eta_min=eta_min)\\n\",\n \"#scheduler = # Create LambdaLR scheduler\\n\",\n \"scheduler = LambdaLR(optimizer, lr_lambda=get_lr_lambda) #get_constant_schedule_with_warmup(optimizer,num_warmup_steps=10)#\\n\",\n \"global_step = 0\\n\",\n \"accumulation_steps = int(512/batch_size)#32\\n\",\n \"model.train()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"j9vN0iRPYbRa\"\n },\n \"outputs\": [],\n \"source\": [\n \"new_checkpoint=\\\"saheedniyi/YarnGPT\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 17,\n \"referenced_widgets\": [\n 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:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\\n\",\n \" checkpoint=torch.load(\\\"/content/drive/MyDrive/model_final/final_0lastxepoch.pt\\\")\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\n\",\n \"#checkpoint=torch.load(\\\"/content/drive/MyDrive/model_final/final_0lastxepoch.pt\\\")\\n\",\n \"#optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\\n\",\n \"#model.load_state_dict(checkpoint['model_state_dict'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"eP0TyvltVUpO\",\n \"outputId\": \"78bc548a-c830-4a46-cafe-71c85eb81489\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"device(type='cuda')\"\n ]\n },\n \"execution_count\": 107,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"device\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": 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\\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for unidic-lite: filename=unidic_lite-1.0.8-py3-none-any.whl size=47658818 sha256=e89007bef2e730232d99961e7a6c9bca61d80047f53366475e01b6c78a72aae1\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/89/e8/68/f9ac36b8cc6c8b3c96888cd57434abed96595d444f42243853\\n\",\n \"Successfully built openai-whisper encodec pesq unidic-lite\\n\",\n \"Installing collected packages: unidic-lite, pesq, mecab-python3, uroman, triton, tensorboardX, loguru, lightning-utilities, jsonargparse, tiktoken, sounddevice, resampy, torchmetrics, openai-whisper, torchcrepe, encodec, pytorch-lightning, outetts\\n\",\n \"Successfully installed encodec-0.1.1 jsonargparse-4.35.0 lightning-utilities-0.11.9 loguru-0.7.3 mecab-python3-1.0.10 openai-whisper-20240930 outetts-0.2.3 pesq-0.0.4 pytorch-lightning-2.5.0.post0 resampy-0.4.3 sounddevice-0.5.1 tensorboardX-2.6.2.2 tiktoken-0.8.0 torchcrepe-0.0.23 torchmetrics-1.6.1 triton-3.1.0 unidic-lite-1.0.8 uroman-1.3.1.1\\n\"\n ]\n }\n ],\n \"source\": [\n \"pip install outetts uroman\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"HgJjekSOT8iX\",\n \"outputId\": \"2f0873ae-60ff-49a3-eb76-f82c36fe2390\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting datasets\\n\",\n \" Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\\n\",\n \"Requirement already satisfied: triton in /usr/local/lib/python3.10/dist-packages (3.1.0)\\n\",\n \"Collecting snac\\n\",\n \" Downloading snac-1.2.1-py3-none-any.whl.metadata (3.5 kB)\\n\",\n \"Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.19.1)\\n\",\n \"Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (1.2.1)\\n\",\n \"Collecting torchdata\\n\",\n \" Downloading 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fsspec-2024.9.0-py3-none-any.whl (179 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m179.3/179.3 kB\\u001b[0m \\u001b[31m17.8 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m134.8/134.8 kB\\u001b[0m \\u001b[31m13.6 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m194.1/194.1 kB\\u001b[0m \\u001b[31m19.0 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, torchdata, snac, datasets\\n\",\n \" Attempting uninstall: fsspec\\n\",\n \" Found existing installation: fsspec 2024.10.0\\n\",\n \" Uninstalling fsspec-2024.10.0:\\n\",\n \" Successfully uninstalled fsspec-2024.10.0\\n\",\n \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n \"gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n \"\\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 snac-1.2.1 torchdata-0.10.1 xxhash-3.5.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install datasets triton snac wandb accelerate torchdata\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"m4uPM3IpnsEo\",\n \"outputId\": \"63a19431-04b3-49d3-da29-1119152ed72e\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[32m2025-01-13 08:34:21.368\\u001b[0m | \\u001b[31m\\u001b[1mERROR \\u001b[0m | \\u001b[36moutetts.version.v1.interface\\u001b[0m:\\u001b[36m\\u001b[0m:\\u001b[36m21\\u001b[0m - \\u001b[31m\\u001b[1mPortAudio library not found\\u001b[0m\\n\",\n \"\\u001b[32m2025-01-13 08:34:21.370\\u001b[0m | \\u001b[33m\\u001b[1mWARNING \\u001b[0m | \\u001b[36moutetts.version.v1.interface\\u001b[0m:\\u001b[36m\\u001b[0m:\\u001b[36m22\\u001b[0m - \\u001b[33m\\u001b[1mFailed to import sounddevice. Audio playback is disabled.\\u001b[0m\\n\"\n ]\n }\n ],\n \"source\": [\n \"from outetts.wav_tokenizer.decoder import WavTokenizer\\n\",\n \"from outetts.wav_tokenizer.encoder.utils import convert_audio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"543a-ZmC7xjE\",\n \"outputId\": \"7b8f7b74-991f-4680-c930-39511544f3af\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Mounted at /content/drive\\n\"\n ]\n }\n ],\n \"source\": [\n \"from google.colab import drive\\n\",\n \"drive.mount('/content/drive')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"EVyBedbQUM3F\"\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"import os\\n\",\n \"import torch\\n\",\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import torchaudio\\n\",\n \"#from snac import SNAC\\n\",\n \"from tqdm import tqdm\\n\",\n \"import huggingface_hub\\n\",\n \"import shutil\\n\",\n \"import soundfile as sf\\n\",\n \"from torch.utils.data import DataLoader, Dataset\\n\",\n \"from transformers import AdamW, get_linear_schedule_with_warmup, DataCollatorWithPadding\\n\",\n \"from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets,Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Z8LFkziTgFRf\"\n },\n \"outputs\": [],\n \"source\": [\n \"import torchaudio\\n\",\n \"import torch\\n\",\n \"import torchaudio.functional as F\\n\",\n \"import inflect\\n\",\n \"import re\\n\",\n \"import uroman as ur\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 17,\n \"referenced_widgets\": [\n \"70f2b1a35f414c798a8300c74e1d1be0\",\n \"02848bb5b7494a4fa7fa9a05aa4ac2bc\"\n ]\n },\n \"id\": \"DN19SQCOUc6m\",\n \"outputId\": \"f602c414-4208-4605-d57b-a0d4a7ce0fff\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"70f2b1a35f414c798a8300c74e1d1be0\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"VBox(children=(HTML(value='
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\"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"AFTfQ4RHIyw8\",\n \"outputId\": \"01e8262a-4981-4cfd-da18-c4f72e99afa6\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1929276, 3)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_data.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"nDWFPH4YI4q-\"\n },\n \"outputs\": [],\n \"source\": [\n \"#train_data=train_data.reset_index(drop=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"t0QWUWIlI-U9\"\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"from datasets import Dataset\\n\",\n \"train_dataset=Dataset.from_pandas(train_data[[\\\"tts\\\"]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"QepGS-a8qSJL\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_dataset=train_dataset.shuffle()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ToMeB3qx6i_V\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_dataset=train_dataset.shuffle()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"train_dataset=train_dataset.shuffle()\"\n ],\n \"metadata\": {\n \"id\": \"t_pjjZL194-5\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"train_dataset=train_dataset.shuffle()\"\n ],\n \"metadata\": {\n \"id\": \"mDN0r319953f\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Eoxv09xaGsM_\"\n },\n \"outputs\": [],\n \"source\": [\n \"from torch.utils.data import DataLoader, Dataset\\n\",\n \"class YarnDataset(Dataset):\\n\",\n \" def __init__(self,dataset):\\n\",\n \" self.ds = dataset\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \" def __len__(self):\\n\",\n \" return len(self.ds)\\n\",\n \"\\n\",\n \"\\n\",\n \" def __getitem__(self, idx):\\n\",\n \" prompt=self.ds[idx][\\\"tts\\\"]\\n\",\n \" #print(prompt)\\n\",\n \" return tokenizer(prompt,)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"PdiQt7_Ctlbb\"\n },\n \"outputs\": [],\n \"source\": [\n \"yarn_dataset = YarnDataset(train_dataset)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"QJBKk_oXQ3HP\"\n },\n \"outputs\": [],\n \"source\": [\n \"batch_size=4\\n\",\n \"learning_rate=1e-3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"qPkuxPZIO1dP\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Initialize data collator\\n\",\n \"data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\\n\",\n \"\\n\",\n \"# Create DataLoader with collate_fn using data collator\\n\",\n \"dataloader = DataLoader(\\n\",\n \" yarn_dataset,\\n\",\n \" batch_size=batch_size,\\n\",\n \" collate_fn=data_collator,shuffle=True # Automatically handles padding\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"XHZZVQBQRaXi\",\n \"outputId\": \"03404215-aa7d-4672-b24b-b8341a747c51\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1929276\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(yarn_dataset)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"mpqTRQC50jEy\",\n \"outputId\": \"e99d7e6f-3334-45b0-f658-6e6a960a4798\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'input_ids': [1, 198, 49152, 14765, 49158, 721, 518, 49158, 10374, 49158, 270, 101, 49158, 93, 49158, 5568, 518, 49158, 3250, 49153, 198, 52184, 198, 49154, 198, 14765, 51291, 49156, 49315, 49378, 49378, 49277, 50451, 49725, 49876, 50336, 49760, 50640, 50226, 49810, 50526, 50135, 49840, 50293, 50282, 50438, 50770, 49707, 50289, 50587, 50532, 50213, 50182, 50464, 50607, 50532, 50258, 50864, 50126, 50648, 49579, 50280, 50519, 49326, 50382, 49839, 50140, 50382, 49958, 50980, 49874, 50473, 50098, 50187, 50135, 50154, 50481, 49710, 49825, 50025, 49595, 49433, 49315, 49324, 49612, 49612, 49612, 49612, 49403, 49378, 50716, 50021, 50726, 50936, 49280, 50774, 49785, 50481, 50469, 50755, 50334, 50070, 50420, 49961, 50269, 50465, 49945, 50283, 49526, 49157, 198, 721, 518, 51237, 49156, 50445, 50343, 49918, 50379, 50003, 49859, 49258, 50792, 49542, 50467, 50190, 50103, 50485, 50193, 50904, 50443, 50653, 49583, 49772, 49667, 50329, 50269, 49715, 50646, 49659, 50228, 49665, 49207, 49739, 50023, 49389, 50360, 49817, 49276, 49581, 49307, 49196, 50079, 49573, 49855, 49191, 49157, 198, 10374, 51201, 49156, 50878, 49461, 50234, 49292, 49210, 49857, 49695, 49452, 49289, 49195, 50586, 49553, 49746, 49157, 198, 270, 101, 44, 108, 100, 79, 32, 30, 34, 32, 108, 46, 49156, 49906, 49269, 50053, 49216, 49865, 50574, 49336, 50777, 49906, 50269, 49673, 50004, 49359, 49805, 50380, 49157, 198, 93, 51219, 49156, 49942, 49497, 49216, 50689, 50854, 50931, 50395, 50226, 50370, 50251, 50468, 50410, 50344, 50556, 49540, 50280, 50480, 50565, 50190, 50364, 50943, 50040, 49866, 50782, 50945, 49930, 50941, 49157, 198, 5568, 518, 51221, 49156, 50167, 49832, 49821, 50893, 50012, 49669, 50338, 49667, 50427, 49317, 49159, 50157, 49474, 50075, 49638, 50143, 49456, 50846, 49586, 49790, 50067, 49341, 50615, 50072, 50017, 50434, 50777, 50194, 50771, 49157, 198, 3250, 51191, 49156, 50037, 50593, 50243, 50090, 50338, 50440, 49157, 198, 49155, 198, 2, 198], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"yarn_dataset[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"87LfDjTFyj6y\",\n \"outputId\": \"f98d4b12-13cb-4ae1-898b-a9080a084e28\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"482319\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(dataloader)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"_NJ4gP3_2Cp-\",\n \"outputId\": \"a3ddfd13-1e34-4686-eb8a-cdcd4655bac7\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"942.029296875\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"482319/512\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"dUtacXKt2Iw1\",\n \"outputId\": \"d800f7b0-6785-4e46-8ddd-522acf58b7ef\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1250.0\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"640000/512\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"M340iQMK2PiF\",\n \"outputId\": \"f61ad845-1472-4cbe-b02a-63e0271aa6d8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"942.029296875\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1929276/(512*4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"xfpWBncA2dK9\",\n \"outputId\": \"27fe6dd5-34ff-40bc-92fc-e3540ffe3857\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"942.029296875\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"482319/512\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"1Ez7HQd7GFEA\"\n },\n \"outputs\": [],\n \"source\": [\n \"from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts\\n\",\n \"from torch.optim.lr_scheduler import LambdaLR\\n\",\n \"from transformers import get_linear_schedule_with_warmup,get_cosine_schedule_with_warmup,get_constant_schedule_with_warmup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"kBWFROqbzO3h\"\n },\n \"outputs\": [],\n \"source\": [\n \"def get_lr_lambda(step):\\n\",\n \" if step < lr_warmup_steps:\\n\",\n \" # Linear warmup\\n\",\n \" return step / lr_warmup_steps\\n\",\n \" elif step >=(num_decay_start):\\n\",\n \" return 1-(step-num_decay_start)/(num_training_steps-num_decay_start)\\n\",\n \" else:\\n\",\n \" # Constant learning rate\\n\",\n \" return 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"18ObtWm1Ryr3\"\n },\n \"outputs\": [],\n \"source\": [\n \"#0.2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"AVI3iLgUbYnW\",\n \"outputId\": \"9e2b09ad-b2e6-4699-b834-c75674b927bc\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"OptimizedModule(\\n\",\n \" (_orig_mod): LlamaForCausalLM(\\n\",\n \" (model): LlamaModel(\\n\",\n \" (embed_tokens): Embedding(53248, 960)\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0-31): 32 x LlamaDecoderLayer(\\n\",\n \" (self_attn): LlamaSdpaAttention(\\n\",\n \" (q_proj): Linear(in_features=960, out_features=960, bias=False)\\n\",\n \" (k_proj): Linear(in_features=960, out_features=320, bias=False)\\n\",\n \" (v_proj): Linear(in_features=960, out_features=320, bias=False)\\n\",\n \" (o_proj): Linear(in_features=960, out_features=960, bias=False)\\n\",\n \" (rotary_emb): LlamaRotaryEmbedding()\\n\",\n \" )\\n\",\n \" (mlp): LlamaMLP(\\n\",\n \" (gate_proj): Linear(in_features=960, out_features=2560, bias=False)\\n\",\n \" (up_proj): Linear(in_features=960, out_features=2560, bias=False)\\n\",\n \" (down_proj): Linear(in_features=2560, out_features=960, bias=False)\\n\",\n \" (act_fn): SiLU()\\n\",\n \" )\\n\",\n \" (input_layernorm): LlamaRMSNorm((960,), eps=1e-05)\\n\",\n \" (post_attention_layernorm): LlamaRMSNorm((960,), eps=1e-05)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (norm): LlamaRMSNorm((960,), eps=1e-05)\\n\",\n \" (rotary_emb): LlamaRotaryEmbedding()\\n\",\n \" )\\n\",\n \" (lm_head): Linear(in_features=960, out_features=53248, bias=False)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"num_epochs=2\\n\",\n \"optimizer = AdamW(model.parameters(), lr=learning_rate, betas=(0.9, 0.95),weight_decay=0.01)\\n\",\n \"lr_warmup_steps=200\\n\",\n \"\\n\",\n \"num_training_steps=1255*num_epochs\\n\",\n \"num_decay_start=50#num_training_steps#-20\\n\",\n \"#scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=T_0, T_mult=T_mult, eta_min=eta_min)\\n\",\n \"#scheduler = # Create LambdaLR scheduler\\n\",\n \"scheduler = get_constant_schedule_with_warmup(optimizer,num_warmup_steps=lr_warmup_steps)#LambdaLR(optimizer, lr_lambda=get_lr_lambda) #get_constant_schedule_with_warmup(optimizer,num_warmup_steps=10)#\\n\",\n \"global_step = 0\\n\",\n \"accumulation_steps = int(512/batch_size)#32\\n\",\n \"model.train()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"j9vN0iRPYbRa\"\n },\n \"outputs\": [],\n \"source\": [\n \"new_checkpoint=\\\"saheedniyi/YarnGPT-local\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 17,\n \"referenced_widgets\": [\n \"3d687f99d4564f4e9efc1f988f5d6799\",\n \"5625d8053fb64e00a587464d8800a25c\"\n ]\n },\n \"id\": \"V0k8jG6Q2iMP\",\n \"outputId\": \"c8665e1c-e876-48de-cd97-82a25086ff0e\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"3d687f99d4564f4e9efc1f988f5d6799\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"VBox(children=(HTML(value='
:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\\n\",\n \" checkpoint=torch.load(\\\"/content/drive/MyDrive/YarnGPT_naij/final_{epoch}epoch.pt\\\")\\n\"\n ]\n },\n {\n \"ename\": \"FileNotFoundError\",\n \"evalue\": \"[Errno 2] No such file or directory: '/content/drive/MyDrive/YarnGPT_naij/final_{epoch}epoch.pt'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mFileNotFoundError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mcheckpoint\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mtorch\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mload\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"/content/drive/MyDrive/YarnGPT_naij/final_{epoch}epoch.pt\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 2\\u001b[0m \\u001b[0moptimizer\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mload_state_dict\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mcheckpoint\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'optimizer_state_dict'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3\\u001b[0m \\u001b[0;31m#model.load_state_dict(checkpoint['model_state_dict'])\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\\u001b[0m in \\u001b[0;36mload\\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\\u001b[0m\\n\\u001b[1;32m 1317\\u001b[0m \\u001b[0mpickle_load_args\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m\\\"encoding\\\"\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m\\\"utf-8\\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1318\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1319\\u001b[0;31m \\u001b[0;32mwith\\u001b[0m \\u001b[0m_open_file_like\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mf\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m\\\"rb\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0mopened_file\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1320\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0m_is_zipfile\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mopened_file\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1321\\u001b[0m \\u001b[0;31m# The zipfile reader is going to advance the current file position.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\\u001b[0m in \\u001b[0;36m_open_file_like\\u001b[0;34m(name_or_buffer, mode)\\u001b[0m\\n\\u001b[1;32m 657\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_open_file_like\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mname_or_buffer\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmode\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 658\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0m_is_path\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mname_or_buffer\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 659\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0m_open_file\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mname_or_buffer\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmode\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 660\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 661\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;34m\\\"w\\\"\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mmode\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\\u001b[0m in \\u001b[0;36m__init__\\u001b[0;34m(self, name, mode)\\u001b[0m\\n\\u001b[1;32m 638\\u001b[0m \\u001b[0;32mclass\\u001b[0m \\u001b[0m_open_file\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0m_opener\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 639\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m__init__\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmode\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 640\\u001b[0;31m \\u001b[0msuper\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__init__\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mopen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m,\\u001b[0m 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"python-wrapper/README.md", "content": "# YarnGPT Python Wrapper Library\n\n## Description\nYarnGPT is a Python wrapper for the YarnGPT text-to-speech model, designed to synthesize natural Nigerian-accented English speech using a pure language modeling approach. This library provides a simple API to convert text into audio output, allowing users to select from various preset voices and adjust generation parameters.\n\n## Features\n- Supports 6 preset voices (idera, jude, joke, umar, osagie, onye)\n- Utilizes Hugging Face's model caching for efficient model loading\n- Exposes a straightforward API function: generate_speech(text, speaker, temperature, repetition_penalty, max_length)\n- Allows customization of generation parameters such as temperature, repetition penalty, and maximum token length\n- Includes unit tests to ensure core functionality\n\n## Installation\n1. Create and activate a virtual environment:\n - On Linux/MacOS:\n ```bash\n python3 -m venv env\n source env/bin/activate\n ```\n - On Windows:\n ```bash\n python -m venv env\n env\\Scripts\\activate\n ```\n\n2. Install the package:\n ```bash\n pip install yarngpt\n ```\n\n## Usage\nBasic usage to generate and save audio:\n```python\nfrom yarngpt import generate_speech\nimport torchaudio\n\n# Generate speech with the default speaker (idera)\naudio = generate_speech(\"Hello, this is a test.\")\n\n# Save the generated audio\ntorchaudio.save(\"output.wav\", audio, sample_rate=24000)\n```\n\nFor Jupyter Notebook users, you can also play the audio directly:\n```python\nfrom yarngpt import generate_speech\nimport torchaudio\nfrom IPython.display import Audio\n\n# Generate and save speech\naudio = generate_speech(\"Hello, this is a test.\", speaker=\"joke\")\ntorchaudio.save(\"output.wav\", audio, sample_rate=24000)\n\n# Play the audio in the notebook\nAudio(\"output.wav\")\n```\n\n## Parameter Options\n- `text`: The input string to convert to speech\n- `speaker`: Choose from available speakers: idera, jude, joke, umar, osagie, onye (default is \"idera\")\n- `temperature`: Controls the randomness of generation (default is 0.1)\n- `repetition_penalty`: A factor to reduce repetitive output (default is 1.1)\n- `max_length`: The maximum length of the generated output tokens (default is 4000)\n\n## Testing\nRun the unit tests to verify functionality:\n```bash\npython -m unittest discover -s tests\n```\n\n\n## License\nThis project is licensed under the MIT License.\n\n## Acknowledgments\n- Built as a contribution to yarngpt projects\n- Utilizes Hugging Face's model caching and the transformers library\n- Special thanks to the open-source community for their ongoing support\n\nFor more details and documentation, visit the GitHub repository: https://github.com/jerryola1\n" }, { "path": "python-wrapper/audiotokenizer.py", "content": "import os\nimport re\nimport json\nimport torch\nimport inflect\nimport random\nimport uroman as ur\nimport numpy as np\nimport torchaudio\nfrom transformers import AutoTokenizer\nfrom outetts.wav_tokenizer.decoder import WavTokenizer\nfrom outetts.wav_tokenizer.encoder.utils import convert_audio\n\nclass AudioTokenizer:\n\n def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{audio_start}\\n\"\n self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n self.bos = \"<|im_start|>\"\n self.eos = \"<|im_end|>\"\n self.input_length=0\n self.special_tokens = {\n \"audio_code\": \"<|{}|>\",\n \"text_start\": \"<|text_start|>\",\n \"text_end\": \"<|text_end|>\",\n \"audio_start\": \"<|audio_start|>\",\n \"audio_end\": \"<|audio_end|>\",\n \"time\": \"<|t_{:.2f}|>\",\n \"code_start\": \"<|code_start|>\",\n \"code_end\": \"<|code_end|>\",\n \"text_sep\": \"<|text_sep|>\"\n }\n self.lec = inflect.engine()\n #self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{audio_start}\\n\"\n #self.config_path = \"/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\"\n #self.model_path = \"/content/wavtokenizer_large_speech_320_24k.ckpt\"\n self.wavtokenizer = WavTokenizer.from_pretrained0802(wav_tokenizer_config_path, wav_tokenizer_model_path)\n self.wavtokenizer = self.wavtokenizer.to(self.device)\n self.BASE_DIR = os.path.dirname(__file__)\n self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, \"default_speakers\")\n self.speakers=[\"idera\",\"emma\",\"onye\",\"jude\",\"osagie\",\"tayo\",\"zainab\",\"joke\",\"regina\",\"remi\",\"umar\",\"chinenye\"]\n\n def get_speaker_path(self,speaker_name):\n return os.path.join(self.DEFAULT_SPEAKERS_DIR, f\"{speaker_name}.json\")\n\n def load_speaker(self, path: str):\n with open(path, \"r\") as f:\n return json.load(f)\n\n def load_default_speaker(self, name: str):\n name = name.lower().strip()\n speaker_path=self.get_speaker_path(name)\n return self.load_speaker(speaker_path)\n\n\n def process_text(self, text: str):\n\n text = re.sub(r'\\d+(\\.\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\n text = re.sub(r'[-_/,\\.\\\\]', ' ', text)\n text = re.sub(r'[^a-z\\s]', '', text)\n text = re.sub(r'\\s+', ' ', text).strip()\n return text.split()\n\n def create_audio_prompt(self,words: list) -> str:\n prompt = []\n for i in words:\n word = i[\"word\"]\n duration = self.special_tokens[\"time\"].format(float(i[\"duration\"]))\n tokens = \"\".join([self.special_tokens[\"audio_code\"].format(c) for c in i[\"codes\"]])\n prompt.append(f'{word}{duration}{self.special_tokens[\"code_start\"]}{tokens}{self.special_tokens[\"code_end\"]}')\n return \"\\n\".join(prompt)\n\n def create_prompt(self,text,speaker_name=\"idera\"):\n speaker=self.load_default_speaker(speaker_name)\n input_words = self.process_text(speaker[\"text\"]) + self.process_text(text)\n #input_words = process_text(speaker[\"text\"]) + input_words\n\n inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n prompt = self.text_prompt.format(\n bos=self.bos,\n text_start=self.special_tokens['text_start'],\n words=inputs_words_strings,\n text_end=self.special_tokens['text_end'],\n audio_start=self.special_tokens['audio_start']\n )\n prompt += self.create_audio_prompt(speaker[\"words\"])\n\n return prompt\n\n def tokenize_prompt(self, prompt):\n input_ids = self.tokenizer.encode(\n prompt,\n add_special_tokens=False,\n return_tensors=\"pt\"\n ).to(self.device)\n self.input_length=input_ids.shape[1]\n return input_ids.to(self.device)\n\n\n def get_audio(self,discrete_code):\n discrete_code=torch.tensor([[discrete_code]]).to(self.device)\n features = self.wavtokenizer.codes_to_features(discrete_code).to(self.device)\n bandwidth_id = torch.tensor([0]).to(self.device)\n audio_out = self.wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n return audio_out.to(\"cpu\")\n\n def extract_integers(self,s):\n # Match integers enclosed in vertical bars |integer|\n matches = re.findall(r'\\|(-?\\d+)\\|', s)\n # Convert matches to integers\n return [int(match) for match in matches]\n\n def get_codes(self, output):\n new_output=self.tokenizer.decode(output[0][self.input_length:])\n codes=self.extract_integers(new_output)\n return codes\n\n\nclass AudioTokenizerForLocal(AudioTokenizer):\n\n def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):\n super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)\n self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{lang}\\n{audio_start}\\n\"\n self.special_tokens = {\n \"audio_code\": \"<|{}|>\",\n \"text_start\": \"<|text_start|>\",\n \"text_end\": \"<|text_end|>\",\n \"audio_start\": \"<|audio_start|>\",\n \"audio_end\": \"<|audio_end|>\",\n \"word_start\": \"<|word_start|>\",\n \"word_end\": \"<|word_end|>\",\n \"time\": \"<|t_{:.2f}|>\",\n \"code_start\": \"<|code_start|>\",\n \"code_end\": \"<|code_end|>\",\n \"text_sep\": \"<|text_sep|>\",\n \"hausa\":\"<|hausa|>\",\n \"igbo\":\"<|igbo|>\",\n \"yoruba\":\"<|yoruba|>\",\n }\n self.uroman = ur.Uroman()\n self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, \"default_speakers_local\")\n self.speakers = [\n \"hausa_male1\", \"hausa_male2\",\"yoruba_male1\", \"yoruba_male2\",\"igbo_male2\" #\"igbo_male1\", \"igbo_male2\",\n \"hausa_female1\", \"hausa_female2\", \"igbo_female1\", \"igbo_female2\", \"yoruba_female1\", \"yoruba_female2\"\n ]\n \n def process_text(self, text: str):\n text = self.uroman.romanize_string(text)\n text = re.sub(r'\\d+(\\.\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\n text = re.sub(r'[-_/,\\.\\\\]', ' ', text)\n text = re.sub(r'[^a-z\\s]', '', text)\n text = re.sub(r'\\s+', ' ', text).strip()\n return text.split()\n\n def create_prompt(self,text,lang,speaker_name=None):\n assert lang in [\"hausa\",\"igbo\",\"yoruba\"], f\"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba']\"\n #if no speaker\n if speaker_name is None:\n if lang==\"hausa\":\n speaker_name=random.choice([\"hausa_male1\",\"hausa_male2\",\"hausa_female1\",\"hausa_female2\"])\n elif lang==\"igbo\":\n speaker_name=random.choice([\"igbo_female1\",\"igbo_female2\",\"igbo_male2\"])#\"igbo_male1\"])\n else:\n speaker_name=random.choice([\"yoruba_male2\",\"yoruba_female1\",\"yoruba_female2\"])\n speaker=self.load_default_speaker(speaker_name)\n input_words = self.process_text(speaker[\"text\"]) + self.process_text(text)\n #input_words = process_text(speaker[\"text\"]) + input_words\n\n inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n prompt = self.text_prompt.format(\n bos=self.bos,\n text_start=self.special_tokens['text_start'],\n words=inputs_words_strings,\n text_end=self.special_tokens['text_end'],\n lang=self.special_tokens[lang],\n audio_start=self.special_tokens['audio_start']\n )\n prompt += self.create_audio_prompt(speaker[\"words\"])\n\n return prompt\n\n\nclass AudioTokenizerV2(AudioTokenizer):\n\n def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):\n super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)\n self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{lang}\\n{audio_start}\\n\"\n self.asr_prompt=\"{bos}\\n{code_start}{codes}{code_end}\\n{asr}\\n\"\n self.special_tokens = {\n \"audio_code\": \"<|{}|>\",\n \"text_start\": \"<|text_start|>\",\n \"text_end\": \"<|text_end|>\",\n \"audio_start\": \"<|audio_start|>\",\n \"audio_end\": \"<|audio_end|>\",\n \"word_start\": \"<|word_start|>\",\n \"word_end\": \"<|word_end|>\",\n \"time\": \"<|t_{:.2f}|>\",\n \"code_start\": \"<|code_start|>\",\n \"code_end\": \"<|code_end|>\",\n \"text_sep\": \"<|text_sep|>\",\n \"hausa\":\"<|hausa|>\",\n \"igbo\":\"<|igbo|>\",\n \"yoruba\":\"<|yoruba|>\",\n \"english\":\"<|english|>\",#<|english|>\n \"asr\":\"<|asr|>\"\n }\n self.uroman = ur.Uroman()\n self.DEFAULT_SPEAKERS_DIR_LOCAL = os.path.join(self.BASE_DIR, \"default_speakers_local\")\n self.DEFAULT_SPEAKERS_ENG = os.path.join(self.BASE_DIR, \"default_speakers\")\n self.speakers_local = [\n \"hausa_male1\", \"hausa_male2\",\"yoruba_male1\", \"yoruba_male2\",\"igbo_male2\" #\"igbo_male1\", \"igbo_male2\",\n \"hausa_female1\", \"hausa_female2\", \"igbo_female1\", \"igbo_female2\", \"yoruba_female1\", \"yoruba_female2\"\n ]\n self.speakers_eng = [\"idera\",\"emma\",\"onye\",\"jude\",\"osagie\",\"tayo\",\"zainab\",\"joke\",\"regina\",\"remi\",\"umar\",\"chinenye\",\"saheed\"]\n self.changed_tokens=[('<|1836|>', '<|453|><|453|>'),\n ('<|1837|>', '<|1836|><|1836|>'),\n ('<|1838|>', '<|1837|><|1837|>'),\n ('<|1840|>', '<|244|><|167|>'),\n ('<|1841|>', '<|235|><|219|>'),\n ('<|1844|>', '<|453|><|244|>'),\n ('<|1845|>', '<|1838|><|1838|>')]\n\n def process_text(self, text: str):\n text = self.uroman.romanize_string(text)\n text = re.sub(r'\\d+(\\.\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\n text = re.sub(r'[-_/,\\.\\\\]', ' ', text)\n text = re.sub(r'[^a-z\\s]', '', text)\n text = re.sub(r'\\s+', ' ', text).strip()\n return text.split()\n\n def get_speaker_path(self,speaker_name,dir):\n return os.path.join(dir, f\"{speaker_name}.json\")\n\n def load_speaker(self, path: str):\n with open(path, \"r\") as f:\n return json.load(f)\n\n def load_default_speaker(self, name: str,dir: str):\n name = name.lower().strip()\n speaker_path=self.get_speaker_path(name,dir)\n return self.load_speaker(speaker_path)\n\n def create_prompt(self,text,lang,speaker_name=None):\n assert lang in [\"hausa\",\"igbo\",\"yoruba\",\"english\"], f\"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba','english']\"\n #if no speaker\n dir=self.DEFAULT_SPEAKERS_DIR_LOCAL\n if speaker_name is None:\n if lang==\"hausa\":\n speaker_name=random.choice([\"hausa_male1\",\"hausa_male2\",\"hausa_female1\",\"hausa_female2\"])\n elif lang==\"igbo\":\n speaker_name=random.choice([\"igbo_female1\",\"igbo_female2\",\"igbo_male2\"])#\"igbo_male1\"])\n elif lang==\"yoruba\":\n speaker_name=random.choice([\"yoruba_male2\",\"yoruba_female1\",\"yoruba_female2\"])\n else:\n speaker_name=random.choice(self.speakers_eng)\n \n if lang==\"english\":\n dir=self.DEFAULT_SPEAKERS_ENG\n speaker=self.load_default_speaker(speaker_name,dir)\n input_words = self.process_text(speaker[\"text\"]) + self.process_text(text)\n #input_words = process_text(speaker[\"text\"]) + input_words\n\n inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n prompt = self.text_prompt.format(\n bos=self.bos,\n text_start=self.special_tokens['text_start'],\n words=inputs_words_strings,\n text_end=self.special_tokens['text_end'],\n lang=self.special_tokens[lang],\n audio_start=self.special_tokens['audio_start']\n )\n prompt += self.create_audio_prompt(speaker[\"words\"])\n\n return prompt\n def replace_tokens(text):\n for pair in self.changed_tokens:\n text=text.replace(pair[0],pair[-1])\n return text \n\n def resample(self,audio: np.ndarray, sr: int, target_sr: int):\n audio = audio.to(dtype=torch.float32)\n #.clone().detach()\n audio = audio.unsqueeze(0)\n # 1 as last arg corresponds to mono audio\n resampled = convert_audio(audio, sr, target_sr, 1)\n return resampled.to(self.device )\n\n def quantize_wavtokenizer(self, path):\n audio_data, sample_rate = torchaudio.load(path)\n audio_data=audio_data.squeeze()\n audio = self.resample(audio_data, sample_rate, 24000).to(self.device)\n bandwidth_id = torch.tensor([0]).to(self.device )\n _, codes = self.wavtokenizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n codes = codes.squeeze(1).to(self.device)#+last_text_token\n res=\"\"\n for code in codes[0].tolist():\n res+=f\"<|{code}|>\"\n return res\n \n def load_asr_prompt(self,audio_path):\n codes=self.quantize_wavtokenizer(audio_path)\n prompt = self.asr_prompt.format(\n bos=self.bos,\n code_start=self.special_tokens['code_start'],\n codes=codes,\n code_end=self.special_tokens['code_end'],\n asr=self.special_tokens[\"asr\"],\n )\n return prompt\n\n def get_asr_results(self,output):\n res=\"\"\n for text in self.tokenizer.decode(output[0]).split(\"<|text_start|>\")[-1].split(\"<|text_end|>\")[0].split(\"\\n\"):\n res+=text.split(\"<|word_start|>\")[-1].split(\"<|word_end|>\")[0]\n res+=\" \"\n return res.strip()" }, { "path": "python-wrapper/default_speakers/.ipynb_checkpoints/Yoruba_prepare_data_naij (2)-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"Rxa73RyKnhy3\",\n \"outputId\": \"aa525021-8667-4b2a-b879-f843eee12d7c\"\n },\n \"outputs\": [\n {\n \"output_type\": 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(pyproject.toml) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for openai-whisper: filename=openai_whisper-20240930-py3-none-any.whl size=803373 sha256=006ff9fec7048daea667dce09ad11d66d09d97d5e27939e2f27c96fd3223ab05\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/dd/4a/1f/d1c4bf3b9133c8168fe617ed979cab7b14fe381d059ffb9d83\\n\",\n \" Building wheel for encodec (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for encodec: filename=encodec-0.1.1-py3-none-any.whl size=45760 sha256=451b0ff87f503b1e3e80ee75873ae179f23b53b055ffcac6e5414d3bdf11dad3\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/fc/36/cb/81af8b985a5f5e0815312d5e52b41263237af07b977e6bcbf3\\n\"\n ]\n }\n ],\n \"source\": [\n \"pip install outetts uroman noisereduce mecab-python3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"HgJjekSOT8iX\"\n },\n \"outputs\": [],\n \"source\": [\n \"!pip install datasets triton snac wandb accelerate torchdata\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"m4uPM3IpnsEo\"\n },\n \"outputs\": [],\n \"source\": [\n \"from outetts.wav_tokenizer.decoder import WavTokenizer\\n\",\n \"from outetts.wav_tokenizer.encoder.utils import convert_audio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"543a-ZmC7xjE\"\n },\n \"outputs\": [],\n \"source\": [\n \"from google.colab import drive\\n\",\n \"drive.mount('/content/drive')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"EVyBedbQUM3F\"\n },\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import torchaudio\\n\",\n \"from snac import SNAC\\n\",\n \"from tqdm import tqdm\\n\",\n \"import huggingface_hub\\n\",\n \"import shutil\\n\",\n \"import soundfile as sf\\n\",\n \"from torch.utils.data import DataLoader, Dataset\\n\",\n \"from transformers import AdamW, get_linear_schedule_with_warmup\\n\",\n \"from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Z8LFkziTgFRf\"\n },\n \"outputs\": [],\n \"source\": [\n \"import torchaudio\\n\",\n \"import torch\\n\",\n \"import torchaudio.functional as F\\n\",\n \"import inflect\\n\",\n \"import re\\n\",\n \"import uroman as ur\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"-wARjdSEUdjy\"\n },\n \"outputs\": [],\n \"source\": [\n \"device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"IYyt-dhuWx9q\"\n },\n \"outputs\": [],\n \"source\": [\n \"config_path = \\\"/content/drive/MyDrive/audio_datasets/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\\\"\\n\",\n \"model_path = \\\"/content/drive/MyDrive/audio_datasets/wavtokenizer_large_speech_320_24k.ckpt\\\"#\\\"/content/wavtokenizer_medium_speech_320_24k_v2.ckpt\\\"\\n\",\n \"wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)\\n\",\n \"wavtokenizer = wavtokenizer.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"TrfYeoWNV6T9\"\n },\n \"outputs\": [],\n \"source\": [\n \"class CTCForcedAlignment:\\n\",\n \"\\n\",\n \" def __init__(self, device: str = None):\\n\",\n \" self.device = torch.device(device if device is not None else \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n \" bundle = torchaudio.pipelines.MMS_FA\\n\",\n \" self.sample_rate = bundle.sample_rate\\n\",\n \" self.model = bundle.get_model(with_star=False).to(self.device)\\n\",\n \" self.LABELS = bundle.get_labels(star=None)\\n\",\n \" self.DICTIONARY = bundle.get_dict(star=None)\\n\",\n \" self.lec = inflect.engine()\\n\",\n \" self.uroman = ur.Uroman()\\n\",\n \" #self.wakati = MeCab.Tagger(\\\"-Owakati\\\")\\n\",\n \" #self.wakati_use = [\\\"ja\\\", \\\"zh\\\", \\\"ko\\\"]\\n\",\n \" #self.languages = languages\\n\",\n \"\\n\",\n \" def process_text(self, text: str):\\n\",\n \" #if language not in self.languages:\\n\",\n \" # raise ValueError(f\\\"Language {language} not supported, supported languages are {self.languages}\\\")\\n\",\n \" text = self.uroman.romanize_string(text)\\n\",\n \" text = re.sub(r'\\\\d+(\\\\.\\\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\\n\",\n \" text = re.sub(r'[-_/,\\\\.\\\\\\\\]', ' ', text)\\n\",\n \" text = re.sub(r'[^a-z\\\\s]', '', text)\\n\",\n \" text = re.sub(r'\\\\s+', ' ', text).strip()\\n\",\n \" return text.split()\\n\",\n \"\\n\",\n \" def _unflatten(self, list_, lengths):\\n\",\n \" assert len(list_) == sum(lengths)\\n\",\n \" i = 0\\n\",\n \" ret = []\\n\",\n \" for l in lengths:\\n\",\n \" ret.append(list_[i : i + l])\\n\",\n \" i += l\\n\",\n \" return ret\\n\",\n \"\\n\",\n \" def get_word(self, waveform, spans, num_frames, transcript):\\n\",\n \" ratio = waveform.size(1) / num_frames\\n\",\n \" x0 = int(ratio * spans[0].start)\\n\",\n \" x1 = int(ratio * spans[-1].end)\\n\",\n \" return {\\\"x0\\\": x0, \\\"x1\\\": x1, \\\"word\\\": transcript}\\n\",\n \"\\n\",\n \" def _extract_world_level(self, aligned_tokens, alignment_scores, transcript):\\n\",\n \" token_spans = F.merge_tokens(aligned_tokens, alignment_scores)\\n\",\n \" word_spans = self._unflatten(token_spans, [len(word) for word in transcript])\\n\",\n \" return word_spans\\n\",\n \"\\n\",\n \" def _align(self, emission, tokens):\\n\",\n \" targets = torch.tensor([tokens], dtype=torch.int32, device=torch.device(\\\"cpu\\\"))\\n\",\n \" alignments, scores = F.forced_align(emission.cpu(), targets, blank=0)\\n\",\n \" alignments, scores = alignments[0], scores[0]\\n\",\n \" scores = scores.exp()\\n\",\n \" return alignments, scores\\n\",\n \"\\n\",\n \" def align(self, waveform,sr, transcript):\\n\",\n \" #waveform, sr = torchaudio.load(audio)\\n\",\n \" #waveform = torch.tensor(waveform)\\n\",\n \" all_codes=quantize_wavtokenizer_ctc(waveform,sampling_rate=sr)\\n\",\n \" if waveform.shape[0] > 1:\\n\",\n \" waveform = waveform.mean(dim=0, keepdim=True)\\n\",\n \" waveform = waveform.float()\\n\",\n \" #print(waveform.shape)\\n\",\n \" #print(sr)\\n\",\n \" waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=self.sample_rate)\\n\",\n \" transcript = self.process_text(transcript)\\n\",\n \"\\n\",\n \" with torch.inference_mode():\\n\",\n \" emission, _ = self.model(waveform.to(self.device))\\n\",\n \"\\n\",\n \" tokenized_transcript = [self.DICTIONARY[c] for word in transcript for c in word]\\n\",\n \" alignments, scores = self._align(emission, tokenized_transcript)\\n\",\n \" word_spans = self._extract_world_level(alignments, scores, transcript)\\n\",\n \" num_frames = emission.size(1)\\n\",\n \"\\n\",\n \" outputs = [\\n\",\n \" self.get_word(waveform, word_spans[i], num_frames, transcript[i])\\n\",\n \" for i in range(len(word_spans))\\n\",\n \" ]\\n\",\n \" #codes=quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000):\\n\",\n \" #audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \"\\n\",\n \" outputs[0][\\\"x0\\\"] = 0\\n\",\n \" #print(waveform.shape)\\n\",\n \" #print(self.sample_rate)\\n\",\n \" for i in range(len(outputs)):\\n\",\n \" output = outputs[i]\\n\",\n \" x0 = output[\\\"x0\\\"]\\n\",\n \"\\n\",\n \" if i == len(outputs) - 1:\\n\",\n \" x1 = output[\\\"x1\\\"]\\n\",\n \" else:\\n\",\n \" x1 = outputs[i + 1][\\\"x0\\\"]\\n\",\n \" outputs[i][\\\"audio\\\"] = waveform[:, x0:x1]\\n\",\n \" outputs[i][\\\"duration\\\"]=len(outputs[i][\\\"audio\\\"][0])/self.sample_rate\\n\",\n \" outputs[i][\\\"codes\\\"]=all_codes[int(x0*75/self.sample_rate) : int(x1*75/self.sample_rate)]#quantize_wavtokenizer_ctc(outputs[i][\\\"audio\\\"],sampling_rate=16000, quantizer=wavtokenizer)\\n\",\n \" #convert waveform to codes\\n\",\n \" #duration Add audio\\n\",\n \" return outputs\\n\",\n \"\\n\",\n \" def free(self):\\n\",\n \" del self.model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"CouG9BMIV6-K\"\n },\n \"outputs\": [],\n \"source\": [\n \"ctc = CTCForcedAlignment(\\\"cuda\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"68rBtr5GUcF2\"\n },\n \"outputs\": [],\n \"source\": [\n \"ctc.DICTIONARY\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"275g7SweCKAe\"\n },\n \"outputs\": [],\n \"source\": [\n \"def resample(audio: np.ndarray, sr: int, target_sr: int):\\n\",\n \"\\n\",\n \" audio = audio.to(dtype=torch.float32)\\n\",\n \" #.clone().detach()\\n\",\n \" audio = audio.unsqueeze(0)\\n\",\n \" # 1 as last arg corresponds to mono audio\\n\",\n \" resampled = convert_audio(audio, sr, target_sr, 1)\\n\",\n \" return resampled.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"N85dYwCmWZG8\"\n },\n \"outputs\": [],\n \"source\": [\n \"def quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000, quantizer=wavtokenizer):\\n\",\n \" #audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \" audio = resample(audio_data, sampling_rate, 24000).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio=audio.squeeze(0)\\n\",\n \" _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\\n\",\n \" codes = codes.squeeze(1).to(device)#+last_text_token\\n\",\n \"\\n\",\n \" return codes[0].tolist()#+last_text_token\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"QgGSndp8AoVW\"\n },\n \"outputs\": [],\n \"source\": [\n \"def resample(audio: np.ndarray, sr: int, target_sr: int):\\n\",\n \"\\n\",\n \" audio =audio.to(dtype=torch.float32)\\n\",\n \" #.clone().detach()\\n\",\n \" audio = audio.unsqueeze(0)\\n\",\n \" # 1 as last arg corresponds to mono audio\\n\",\n \" resampled = convert_audio(audio, sr, target_sr, 1)\\n\",\n \" return resampled.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"txxV2uboCYih\"\n },\n \"outputs\": [],\n \"source\": [\n \"def quantize_wavtokenizer(row, quantizer=wavtokenizer):\\n\",\n \" audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \" audio = resample(audio_data, sample_rate, 24000).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" #print(audio.shape)\\n\",\n \" #print(audio.dim())\\n\",\n \" _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\\n\",\n \" codes = codes.squeeze(1).to(device)#+last_text_token\\n\",\n \"\\n\",\n \" return codes[0].tolist()#+last_text_token\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"JDfRH6HUIGiX\"\n },\n \"outputs\": [],\n \"source\": [\n \"def decode_tokenizer(discrete_code):\\n\",\n \" #discrete code is a list\\n\",\n \" discrete_code=torch.tensor([discrete_code]).to(device)-last_text_token\\n\",\n \" features = wavtokenizer.codes_to_features(discrete_code).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\\n\",\n \" return audio_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"0U_45AQey40V\"\n },\n \"outputs\": [],\n \"source\": [\n \"def decode_tokenizer(discrete_code):\\n\",\n \" #discrete code is a list\\n\",\n \" discrete_code=torch.tensor([[discrete_code]]).to(device)#-last_text_token\\n\",\n \" features = wavtokenizer.codes_to_features(discrete_code).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\\n\",\n \" return audio_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ij19rZw-fEQ0\"\n },\n \"outputs\": [],\n \"source\": [\n \"class PromptProcessor():\\n\",\n \" def __init__(self,lang):\\n\",\n \" self.lang=lang\\n\",\n \" self.bos = \\\"<|im_start|>\\\"\\n\",\n \" self.eos = \\\"<|im_end|>\\\"\\n\",\n \" self.tts_prompt = \\\"{bos}\\\\n{tts}\\\\n{text_start}{words}{text_end}\\\\n{lang}\\\\n{audio_start}\\\\n\\\"\\n\",\n \" self.stt_prompt = \\\"{bos}\\\\n{stt}\\\\n{audio_start}{codes}{audio_end}\\\\n{lang}\\\\n{text_start}\\\\n\\\"\\n\",\n \" self.special_tokens = {\\n\",\n \" \\\"audio_code\\\": \\\"<|{}|>\\\",\\n\",\n \" \\\"tts\\\":\\\"<|tts|>\\\",\\n\",\n \" \\\"stt\\\":\\\"<|stt|>\\\",\\n\",\n \" \\\"text_start\\\": \\\"<|text_start|>\\\",\\n\",\n \" \\\"text_end\\\": \\\"<|text_end|>\\\",\\n\",\n \" \\\"audio_start\\\": \\\"<|audio_start|>\\\",\\n\",\n \" \\\"audio_end\\\": \\\"<|audio_end|>\\\",\\n\",\n \" \\\"word_start\\\": \\\"<|word_start|>\\\",\\n\",\n \" \\\"word_end\\\": \\\"<|word_end|>\\\",\\n\",\n \" \\\"time\\\": \\\"<|t_{:.2f}|>\\\",\\n\",\n \" \\\"code_start\\\": \\\"<|code_start|>\\\",\\n\",\n \" \\\"code_end\\\": \\\"<|code_end|>\\\",\\n\",\n \" \\\"text_sep\\\": \\\"<|text_sep|>\\\",\\n\",\n \" \\\"hausa\\\":\\\"<|hausa|\\\">,\\n\",\n \" \\\"igbo\\\":\\\"<|igbo|\\\">,\\n\",\n \" \\\"yoruba\\\":\\\"<|yoruba|>\\\",\\n\",\n \"\\n\",\n \" }\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \"\\n\",\n \" def create_results_prompts(self,words):\\n\",\n \" prompt_audio= []\\n\",\n \" prompt_text=[]\\n\",\n \" all_tokens=[]\\n\",\n \" for i in words:\\n\",\n \" word = i[\\\"word\\\"]\\n\",\n \" duration = self.special_tokens[\\\"time\\\"].format(i[\\\"duration\\\"])\\n\",\n \" tokens = \\\"\\\".join([self.special_tokens[\\\"audio_code\\\"].format(c) for c in i[\\\"codes\\\"]])\\n\",\n \" all_tokens.append(tokens)\\n\",\n \" prompt_audio.append(f'{word}{duration}{self.special_tokens[\\\"code_start\\\"]}{tokens}{self.special_tokens[\\\"code_end\\\"]}')\\n\",\n \" prompt_text.append(f'{tokens}{duration}{self.special_tokens[\\\"word_start\\\"]}{word}{self.special_tokens[\\\"word_end\\\"]}')\\n\",\n \" return \\\"\\\".join(all_tokens),\\\"\\\\n\\\".join(prompt_audio),\\\"\\\\n\\\".join(prompt_text)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" def get_prompt(self, row):\\n\",\n \" try:\\n\",\n \" audio=torch.from_numpy(row[\\\"audio\\\"][\\\"array\\\"]).unsqueeze(0)#torch.tensor([row[\\\"audio\\\"][\\\"array\\\"]])\\n\",\n \" #print(audio)\\n\",\n \" sample_rate=row[\\\"audio\\\"][\\\"sampling_rate\\\"]\\n\",\n \" if row[\\\"text\\\"]:\\n\",\n \" transcript=row[\\\"text\\\"]\\n\",\n \" else:\\n\",\n \" transcript=row[\\\"transcript\\\"]\\n\",\n \" input_words = ctc.process_text(transcript)\\n\",\n \" words= ctc.align(audio,sample_rate,transcript)\\n\",\n \" #print(words)\\n\",\n \" inputs_words_strings = f\\\"{self.special_tokens['text_sep']}\\\".join([i.strip() for i in input_words])\\n\",\n \" #self.text_prompt = \\\"{bos}\\\\n{text_start}{words}{text_end}\\\\n{audio_start}\\\\n\\\"\\n\",\n \" prompt_tts= self.tts_prompt.format(\\n\",\n \" bos=self.bos,\\n\",\n \" text_start=self.special_tokens['text_start'],\\n\",\n \" tts=self.special_tokens['tts'],\\n\",\n \" words=inputs_words_strings,\\n\",\n \" lang=self.special_tokens[self.lang],\\n\",\n \" text_end=self.special_tokens['text_end'],\\n\",\n \" audio_start=self.special_tokens['audio_start']\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \" all_codes, tts_extra, stt_extra=self.create_results_prompts(words)\\n\",\n \" prompt_stt=self.stt_prompt.format(\\n\",\n \" bos=self.bos,\\n\",\n \" audio_start=self.special_tokens['audio_start'],\\n\",\n \" stt=self.special_tokens['stt'],\\n\",\n \" codes=all_codes,\\n\",\n \" lang=self.special_tokens[self.lang],\\n\",\n \"\\n\",\n \" audio_end=self.special_tokens['audio_end'],\\n\",\n \" text_start=self.special_tokens['text_start']\\n\",\n \" )\\n\",\n \" prompt_stt+=stt_extra+f\\\"\\\\n{self.special_tokens['text_end']}\\\\n{self.eos}\\\\n\\\"\\n\",\n \" prompt_tts+=tts_extra+f\\\"\\\\n{self.special_tokens['audio_end']}\\\\n{self.eos}\\\\n\\\"\\n\",\n \"\\n\",\n \" return {\\\"stt\\\":prompt_stt,\\\"tts\\\":prompt_tts}\\n\",\n \" except Exception as e:\\n\",\n \" #print(e)\\n\",\n \" return {\\\"stt\\\":\\\"An error occurred\\\",\\\"tts\\\":\\\"An error occurred\\\"}#,\\\"An error occured\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ctohbEGTfZYq\"\n },\n \"outputs\": [],\n \"source\": [\n \"ps=PromptProcessor(\\\"yoruba\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 17,\n \"referenced_widgets\": [\n \"9f80b9ce82aa4c2bb3e6da8edb4887ef\",\n \"4d77ee1fa6ed43efa05683b12cf26239\"\n ]\n },\n \"id\": \"Q7R28b7gd-9f\",\n \"outputId\": \"0c44d8ba-582f-42ca-f859-acb9a52a5729\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"9f80b9ce82aa4c2bb3e6da8edb4887ef\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"VBox(children=(HTML(value='
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(pyproject.toml) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for openai-whisper: filename=openai_whisper-20240930-py3-none-any.whl size=803373 sha256=006ff9fec7048daea667dce09ad11d66d09d97d5e27939e2f27c96fd3223ab05\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/dd/4a/1f/d1c4bf3b9133c8168fe617ed979cab7b14fe381d059ffb9d83\\n\",\n \" Building wheel for encodec (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for encodec: filename=encodec-0.1.1-py3-none-any.whl size=45760 sha256=451b0ff87f503b1e3e80ee75873ae179f23b53b055ffcac6e5414d3bdf11dad3\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/fc/36/cb/81af8b985a5f5e0815312d5e52b41263237af07b977e6bcbf3\\n\"\n ]\n }\n ],\n \"source\": [\n \"pip install outetts uroman noisereduce mecab-python3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"HgJjekSOT8iX\"\n },\n \"outputs\": [],\n \"source\": [\n \"!pip install datasets triton snac wandb accelerate torchdata\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"m4uPM3IpnsEo\"\n },\n \"outputs\": [],\n \"source\": [\n \"from outetts.wav_tokenizer.decoder import WavTokenizer\\n\",\n \"from outetts.wav_tokenizer.encoder.utils import convert_audio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"543a-ZmC7xjE\"\n },\n \"outputs\": [],\n \"source\": [\n \"from google.colab import drive\\n\",\n \"drive.mount('/content/drive')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"EVyBedbQUM3F\"\n },\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import torchaudio\\n\",\n \"from snac import SNAC\\n\",\n \"from tqdm import tqdm\\n\",\n \"import huggingface_hub\\n\",\n \"import shutil\\n\",\n \"import soundfile as sf\\n\",\n \"from torch.utils.data import DataLoader, Dataset\\n\",\n \"from transformers import AdamW, get_linear_schedule_with_warmup\\n\",\n \"from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Z8LFkziTgFRf\"\n },\n \"outputs\": [],\n \"source\": [\n \"import torchaudio\\n\",\n \"import torch\\n\",\n \"import torchaudio.functional as F\\n\",\n \"import inflect\\n\",\n \"import re\\n\",\n \"import uroman as ur\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"-wARjdSEUdjy\"\n },\n \"outputs\": [],\n \"source\": [\n \"device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"IYyt-dhuWx9q\"\n },\n \"outputs\": [],\n \"source\": [\n \"config_path = \\\"/content/drive/MyDrive/audio_datasets/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\\\"\\n\",\n \"model_path = \\\"/content/drive/MyDrive/audio_datasets/wavtokenizer_large_speech_320_24k.ckpt\\\"#\\\"/content/wavtokenizer_medium_speech_320_24k_v2.ckpt\\\"\\n\",\n \"wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)\\n\",\n \"wavtokenizer = wavtokenizer.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"TrfYeoWNV6T9\"\n },\n \"outputs\": [],\n \"source\": [\n \"class CTCForcedAlignment:\\n\",\n \"\\n\",\n \" def __init__(self, device: str = None):\\n\",\n \" self.device = torch.device(device if device is not None else \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n \" bundle = torchaudio.pipelines.MMS_FA\\n\",\n \" self.sample_rate = bundle.sample_rate\\n\",\n \" self.model = bundle.get_model(with_star=False).to(self.device)\\n\",\n \" self.LABELS = bundle.get_labels(star=None)\\n\",\n \" self.DICTIONARY = bundle.get_dict(star=None)\\n\",\n \" self.lec = inflect.engine()\\n\",\n \" self.uroman = ur.Uroman()\\n\",\n \" #self.wakati = MeCab.Tagger(\\\"-Owakati\\\")\\n\",\n \" #self.wakati_use = [\\\"ja\\\", \\\"zh\\\", \\\"ko\\\"]\\n\",\n \" #self.languages = languages\\n\",\n \"\\n\",\n \" def process_text(self, text: str):\\n\",\n \" #if language not in self.languages:\\n\",\n \" # raise ValueError(f\\\"Language {language} not supported, supported languages are {self.languages}\\\")\\n\",\n \" text = self.uroman.romanize_string(text)\\n\",\n \" text = re.sub(r'\\\\d+(\\\\.\\\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\\n\",\n \" text = re.sub(r'[-_/,\\\\.\\\\\\\\]', ' ', text)\\n\",\n \" text = re.sub(r'[^a-z\\\\s]', '', text)\\n\",\n \" text = re.sub(r'\\\\s+', ' ', text).strip()\\n\",\n \" return text.split()\\n\",\n \"\\n\",\n \" def _unflatten(self, list_, lengths):\\n\",\n \" assert len(list_) == sum(lengths)\\n\",\n \" i = 0\\n\",\n \" ret = []\\n\",\n \" for l in lengths:\\n\",\n \" ret.append(list_[i : i + l])\\n\",\n \" i += l\\n\",\n \" return ret\\n\",\n \"\\n\",\n \" def get_word(self, waveform, spans, num_frames, transcript):\\n\",\n \" ratio = waveform.size(1) / num_frames\\n\",\n \" x0 = int(ratio * spans[0].start)\\n\",\n \" x1 = int(ratio * spans[-1].end)\\n\",\n \" return {\\\"x0\\\": x0, \\\"x1\\\": x1, \\\"word\\\": transcript}\\n\",\n \"\\n\",\n \" def _extract_world_level(self, aligned_tokens, alignment_scores, transcript):\\n\",\n \" token_spans = F.merge_tokens(aligned_tokens, alignment_scores)\\n\",\n \" word_spans = self._unflatten(token_spans, [len(word) for word in transcript])\\n\",\n \" return word_spans\\n\",\n \"\\n\",\n \" def _align(self, emission, tokens):\\n\",\n \" targets = torch.tensor([tokens], dtype=torch.int32, device=torch.device(\\\"cpu\\\"))\\n\",\n \" alignments, scores = F.forced_align(emission.cpu(), targets, blank=0)\\n\",\n \" alignments, scores = alignments[0], scores[0]\\n\",\n \" scores = scores.exp()\\n\",\n \" return alignments, scores\\n\",\n \"\\n\",\n \" def align(self, waveform,sr, transcript):\\n\",\n \" #waveform, sr = torchaudio.load(audio)\\n\",\n \" #waveform = torch.tensor(waveform)\\n\",\n \" all_codes=quantize_wavtokenizer_ctc(waveform,sampling_rate=sr)\\n\",\n \" if waveform.shape[0] > 1:\\n\",\n \" waveform = waveform.mean(dim=0, keepdim=True)\\n\",\n \" waveform = waveform.float()\\n\",\n \" #print(waveform.shape)\\n\",\n \" #print(sr)\\n\",\n \" waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=self.sample_rate)\\n\",\n \" transcript = self.process_text(transcript)\\n\",\n \"\\n\",\n \" with torch.inference_mode():\\n\",\n \" emission, _ = self.model(waveform.to(self.device))\\n\",\n \"\\n\",\n \" tokenized_transcript = [self.DICTIONARY[c] for word in transcript for c in word]\\n\",\n \" alignments, scores = self._align(emission, tokenized_transcript)\\n\",\n \" word_spans = self._extract_world_level(alignments, scores, transcript)\\n\",\n \" num_frames = emission.size(1)\\n\",\n \"\\n\",\n \" outputs = [\\n\",\n \" self.get_word(waveform, word_spans[i], num_frames, transcript[i])\\n\",\n \" for i in range(len(word_spans))\\n\",\n \" ]\\n\",\n \" #codes=quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000):\\n\",\n \" #audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \"\\n\",\n \" outputs[0][\\\"x0\\\"] = 0\\n\",\n \" #print(waveform.shape)\\n\",\n \" #print(self.sample_rate)\\n\",\n \" for i in range(len(outputs)):\\n\",\n \" output = outputs[i]\\n\",\n \" x0 = output[\\\"x0\\\"]\\n\",\n \"\\n\",\n \" if i == len(outputs) - 1:\\n\",\n \" x1 = output[\\\"x1\\\"]\\n\",\n \" else:\\n\",\n \" x1 = outputs[i + 1][\\\"x0\\\"]\\n\",\n \" outputs[i][\\\"audio\\\"] = waveform[:, x0:x1]\\n\",\n \" outputs[i][\\\"duration\\\"]=len(outputs[i][\\\"audio\\\"][0])/self.sample_rate\\n\",\n \" outputs[i][\\\"codes\\\"]=all_codes[int(x0*75/self.sample_rate) : int(x1*75/self.sample_rate)]#quantize_wavtokenizer_ctc(outputs[i][\\\"audio\\\"],sampling_rate=16000, quantizer=wavtokenizer)\\n\",\n \" #convert waveform to codes\\n\",\n \" #duration Add audio\\n\",\n \" return outputs\\n\",\n \"\\n\",\n \" def free(self):\\n\",\n \" del self.model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"CouG9BMIV6-K\"\n },\n \"outputs\": [],\n \"source\": [\n \"ctc = CTCForcedAlignment(\\\"cuda\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"68rBtr5GUcF2\"\n },\n \"outputs\": [],\n \"source\": [\n \"ctc.DICTIONARY\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"275g7SweCKAe\"\n },\n \"outputs\": [],\n \"source\": [\n \"def resample(audio: np.ndarray, sr: int, target_sr: int):\\n\",\n \"\\n\",\n \" audio = audio.to(dtype=torch.float32)\\n\",\n \" #.clone().detach()\\n\",\n \" audio = audio.unsqueeze(0)\\n\",\n \" # 1 as last arg corresponds to mono audio\\n\",\n \" resampled = convert_audio(audio, sr, target_sr, 1)\\n\",\n \" return resampled.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"N85dYwCmWZG8\"\n },\n \"outputs\": [],\n \"source\": [\n \"def quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000, quantizer=wavtokenizer):\\n\",\n \" #audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \" audio = resample(audio_data, sampling_rate, 24000).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio=audio.squeeze(0)\\n\",\n \" _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\\n\",\n \" codes = codes.squeeze(1).to(device)#+last_text_token\\n\",\n \"\\n\",\n \" return codes[0].tolist()#+last_text_token\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"QgGSndp8AoVW\"\n },\n \"outputs\": [],\n \"source\": [\n \"def resample(audio: np.ndarray, sr: int, target_sr: int):\\n\",\n \"\\n\",\n \" audio =audio.to(dtype=torch.float32)\\n\",\n \" #.clone().detach()\\n\",\n \" audio = audio.unsqueeze(0)\\n\",\n \" # 1 as last arg corresponds to mono audio\\n\",\n \" resampled = convert_audio(audio, sr, target_sr, 1)\\n\",\n \" return resampled.to(device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"txxV2uboCYih\"\n },\n \"outputs\": [],\n \"source\": [\n \"def quantize_wavtokenizer(row, quantizer=wavtokenizer):\\n\",\n \" audio_data, sample_rate = row[\\\"audio\\\"][\\\"array\\\"], int(row[\\\"audio\\\"][\\\"sampling_rate\\\"])\\n\",\n \"\\n\",\n \" audio = resample(audio_data, sample_rate, 24000).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" #print(audio.shape)\\n\",\n \" #print(audio.dim())\\n\",\n \" _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\\n\",\n \" codes = codes.squeeze(1).to(device)#+last_text_token\\n\",\n \"\\n\",\n \" return codes[0].tolist()#+last_text_token\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"JDfRH6HUIGiX\"\n },\n \"outputs\": [],\n \"source\": [\n \"def decode_tokenizer(discrete_code):\\n\",\n \" #discrete code is a list\\n\",\n \" discrete_code=torch.tensor([discrete_code]).to(device)-last_text_token\\n\",\n \" features = wavtokenizer.codes_to_features(discrete_code).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\\n\",\n \" return audio_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"0U_45AQey40V\"\n },\n \"outputs\": [],\n \"source\": [\n \"def decode_tokenizer(discrete_code):\\n\",\n \" #discrete code is a list\\n\",\n \" discrete_code=torch.tensor([[discrete_code]]).to(device)#-last_text_token\\n\",\n \" features = wavtokenizer.codes_to_features(discrete_code).to(device)\\n\",\n \" bandwidth_id = torch.tensor([0]).to(device)\\n\",\n \" audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\\n\",\n \" return audio_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ij19rZw-fEQ0\"\n },\n \"outputs\": [],\n \"source\": [\n \"class PromptProcessor():\\n\",\n \" def __init__(self,lang):\\n\",\n \" self.lang=lang\\n\",\n \" self.bos = \\\"<|im_start|>\\\"\\n\",\n \" self.eos = \\\"<|im_end|>\\\"\\n\",\n \" self.tts_prompt = \\\"{bos}\\\\n{tts}\\\\n{text_start}{words}{text_end}\\\\n{lang}\\\\n{audio_start}\\\\n\\\"\\n\",\n \" self.stt_prompt = \\\"{bos}\\\\n{stt}\\\\n{audio_start}{codes}{audio_end}\\\\n{lang}\\\\n{text_start}\\\\n\\\"\\n\",\n \" self.special_tokens = {\\n\",\n \" \\\"audio_code\\\": \\\"<|{}|>\\\",\\n\",\n \" \\\"tts\\\":\\\"<|tts|>\\\",\\n\",\n \" \\\"stt\\\":\\\"<|stt|>\\\",\\n\",\n \" \\\"text_start\\\": \\\"<|text_start|>\\\",\\n\",\n \" \\\"text_end\\\": \\\"<|text_end|>\\\",\\n\",\n \" \\\"audio_start\\\": \\\"<|audio_start|>\\\",\\n\",\n \" \\\"audio_end\\\": \\\"<|audio_end|>\\\",\\n\",\n \" \\\"word_start\\\": \\\"<|word_start|>\\\",\\n\",\n \" \\\"word_end\\\": \\\"<|word_end|>\\\",\\n\",\n \" \\\"time\\\": \\\"<|t_{:.2f}|>\\\",\\n\",\n \" \\\"code_start\\\": \\\"<|code_start|>\\\",\\n\",\n \" \\\"code_end\\\": \\\"<|code_end|>\\\",\\n\",\n \" \\\"text_sep\\\": \\\"<|text_sep|>\\\",\\n\",\n \" \\\"hausa\\\":\\\"<|hausa|\\\">,\\n\",\n \" \\\"igbo\\\":\\\"<|igbo|\\\">,\\n\",\n \" \\\"yoruba\\\":\\\"<|yoruba|>\\\",\\n\",\n \"\\n\",\n \" }\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \"\\n\",\n \" def create_results_prompts(self,words):\\n\",\n \" prompt_audio= []\\n\",\n \" prompt_text=[]\\n\",\n \" all_tokens=[]\\n\",\n \" for i in words:\\n\",\n \" word = i[\\\"word\\\"]\\n\",\n \" duration = self.special_tokens[\\\"time\\\"].format(i[\\\"duration\\\"])\\n\",\n \" tokens = \\\"\\\".join([self.special_tokens[\\\"audio_code\\\"].format(c) for c in i[\\\"codes\\\"]])\\n\",\n \" all_tokens.append(tokens)\\n\",\n \" prompt_audio.append(f'{word}{duration}{self.special_tokens[\\\"code_start\\\"]}{tokens}{self.special_tokens[\\\"code_end\\\"]}')\\n\",\n \" prompt_text.append(f'{tokens}{duration}{self.special_tokens[\\\"word_start\\\"]}{word}{self.special_tokens[\\\"word_end\\\"]}')\\n\",\n \" return \\\"\\\".join(all_tokens),\\\"\\\\n\\\".join(prompt_audio),\\\"\\\\n\\\".join(prompt_text)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" def get_prompt(self, row):\\n\",\n \" try:\\n\",\n \" audio=torch.from_numpy(row[\\\"audio\\\"][\\\"array\\\"]).unsqueeze(0)#torch.tensor([row[\\\"audio\\\"][\\\"array\\\"]])\\n\",\n \" #print(audio)\\n\",\n \" sample_rate=row[\\\"audio\\\"][\\\"sampling_rate\\\"]\\n\",\n \" if row[\\\"text\\\"]:\\n\",\n \" transcript=row[\\\"text\\\"]\\n\",\n \" else:\\n\",\n \" transcript=row[\\\"transcript\\\"]\\n\",\n \" input_words = ctc.process_text(transcript)\\n\",\n \" words= ctc.align(audio,sample_rate,transcript)\\n\",\n \" #print(words)\\n\",\n \" inputs_words_strings = f\\\"{self.special_tokens['text_sep']}\\\".join([i.strip() for i in input_words])\\n\",\n \" #self.text_prompt = \\\"{bos}\\\\n{text_start}{words}{text_end}\\\\n{audio_start}\\\\n\\\"\\n\",\n \" prompt_tts= self.tts_prompt.format(\\n\",\n \" bos=self.bos,\\n\",\n \" text_start=self.special_tokens['text_start'],\\n\",\n \" tts=self.special_tokens['tts'],\\n\",\n \" words=inputs_words_strings,\\n\",\n \" lang=self.special_tokens[self.lang],\\n\",\n \" text_end=self.special_tokens['text_end'],\\n\",\n \" audio_start=self.special_tokens['audio_start']\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \" all_codes, tts_extra, stt_extra=self.create_results_prompts(words)\\n\",\n \" prompt_stt=self.stt_prompt.format(\\n\",\n \" bos=self.bos,\\n\",\n \" audio_start=self.special_tokens['audio_start'],\\n\",\n \" stt=self.special_tokens['stt'],\\n\",\n \" codes=all_codes,\\n\",\n \" lang=self.special_tokens[self.lang],\\n\",\n \"\\n\",\n \" audio_end=self.special_tokens['audio_end'],\\n\",\n \" text_start=self.special_tokens['text_start']\\n\",\n \" )\\n\",\n \" prompt_stt+=stt_extra+f\\\"\\\\n{self.special_tokens['text_end']}\\\\n{self.eos}\\\\n\\\"\\n\",\n \" prompt_tts+=tts_extra+f\\\"\\\\n{self.special_tokens['audio_end']}\\\\n{self.eos}\\\\n\\\"\\n\",\n \"\\n\",\n \" return {\\\"stt\\\":prompt_stt,\\\"tts\\\":prompt_tts}\\n\",\n \" except Exception as e:\\n\",\n \" #print(e)\\n\",\n \" return {\\\"stt\\\":\\\"An error occurred\\\",\\\"tts\\\":\\\"An error occurred\\\"}#,\\\"An error occured\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ctohbEGTfZYq\"\n },\n \"outputs\": [],\n \"source\": [\n \"ps=PromptProcessor(\\\"yoruba\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 17,\n \"referenced_widgets\": [\n \"9f80b9ce82aa4c2bb3e6da8edb4887ef\",\n \"4d77ee1fa6ed43efa05683b12cf26239\"\n ]\n },\n \"id\": \"Q7R28b7gd-9f\",\n \"outputId\": \"0c44d8ba-582f-42ca-f859-acb9a52a5729\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"9f80b9ce82aa4c2bb3e6da8edb4887ef\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"VBox(children=(HTML(value='
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]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ps.get_prompt(k)[\\\"tts\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"43YFGwbEbWkN\"\n },\n \"outputs\": [],\n \"source\": [\n \"data_yoruba = data_yoruba.cast_column(\\\"audio\\\", Audio(sampling_rate=24000))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"6aDesKzcQZQn\",\n \"outputId\": \"455497c1-814c-40f8-a6b4-c9812880aa96\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Dataset({\\n\",\n \" features: ['audio', 'text'],\\n\",\n \" num_rows: 15188\\n\",\n \"})\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_yoruba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n 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1587,\n 1685,\n 1729\n ]\n },\n {\n \"word\": \"like\",\n \"duration\": 0.28,\n \"codes\": [\n 1407,\n 1444,\n 1286,\n 1506,\n 1366,\n 1286,\n 1013,\n 502,\n 631,\n 1449,\n 1374,\n 1711,\n 1413,\n 1660,\n 1679,\n 1783,\n 1772,\n 1723,\n 1549,\n 1674,\n 1388\n ]\n },\n {\n \"word\": \"mister\",\n \"duration\": 0.84,\n \"codes\": [\n 1591,\n 1765,\n 1653,\n 1549,\n 1449,\n 1341,\n 473,\n 1363,\n 1605,\n 1554,\n 1387,\n 1641,\n 1439,\n 362,\n 1606,\n 319,\n 1691,\n 1582,\n 1617,\n 1756,\n 1286,\n 1409,\n 1221,\n 1372,\n 1584,\n 794,\n 1636,\n 1488,\n 1280,\n 1366,\n 1753,\n 1636,\n 882,\n 1723,\n 1796,\n 1769,\n 1717,\n 1549,\n 1518,\n 1633,\n 175,\n 1678,\n 1679,\n 1549,\n 1732,\n 1710,\n 1662,\n 1744,\n 1641,\n 1696,\n 1565,\n 1769,\n 1789,\n 719,\n 1831,\n 1786,\n 1451,\n 1728,\n 1646,\n 1713,\n 1672,\n 1774,\n 1734\n ]\n },\n {\n \"word\": \"so\",\n \"duration\": 0.14,\n \"codes\": [\n 1354,\n 1518,\n 1791,\n 1374,\n 277,\n 1542,\n 1366,\n 700,\n 1444,\n 1744,\n 1217\n ]\n },\n {\n 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239,\n 1810,\n 1778,\n 719,\n 1616,\n 1549,\n 519,\n 1804,\n 1416,\n 1636,\n 1584,\n 1437,\n 1698,\n 1625,\n 1494,\n 1633,\n 1545,\n 1747,\n 1737,\n 1672,\n 1646,\n 1778\n ]\n }\n ]\n}" }, { "path": "python-wrapper/default_speakers/jude.json", "content": "{\n \"text\": \"know what I'm saying what I'm saying is that if you say\",\n \"words\": [\n {\n \"word\": \"know\",\n \"duration\": 0.44,\n \"codes\": [\n 1824,\n 1820,\n 1743,\n 1819,\n 1171,\n 1796,\n 1613,\n 1126,\n 1500,\n 1346,\n 1429,\n 1810,\n 1655,\n 1462,\n 1780,\n 1812,\n 1518,\n 1431,\n 741,\n 1206,\n 1325,\n 1392,\n 920,\n 409,\n 4,\n 1270,\n 416,\n 1759,\n 1141,\n 708,\n 1022,\n 1769,\n 1384\n ]\n },\n {\n \"word\": \"what\",\n \"duration\": 0.12,\n \"codes\": [\n 607,\n 787,\n 48,\n 1350,\n 1340,\n 297,\n 364,\n 825,\n 1775\n ]\n },\n {\n \"word\": \"im\",\n \"duration\": 0.1,\n \"codes\": [\n 1668,\n 1311,\n 1651,\n 1048,\n 176,\n 430,\n 333\n ]\n },\n {\n \"word\": \"saying\",\n \"duration\": 0.56,\n \"codes\": [\n 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\"duration\": 0.24,\n \"codes\": [\n 1773,\n 1004,\n 1796,\n 907,\n 239,\n 1804,\n 565,\n 1432,\n 1534,\n 1718,\n 1643,\n 1432,\n 1447,\n 1273,\n 1824,\n 1657,\n 1776,\n 1651\n ]\n },\n {\n \"word\": \"if\",\n \"duration\": 0.12,\n \"codes\": [\n 1649,\n 1620,\n 1342,\n 176,\n 1773,\n 178,\n 1710,\n 1710,\n 1521\n ]\n },\n {\n \"word\": \"you\",\n \"duration\": 0.16,\n \"codes\": [\n 959,\n 1728,\n 1651,\n 361,\n 822,\n 1661,\n 1341,\n 780,\n 1518,\n 335,\n 452,\n 736\n ]\n },\n {\n \"word\": \"say\",\n \"duration\": 0.14,\n \"codes\": [\n 372,\n 1217,\n 713,\n 848,\n 1140,\n 1420,\n 1549,\n 483,\n 125,\n 1353\n ]\n }\n ]\n}" }, { "path": "python-wrapper/default_speakers/onye.json", "content": "{\n \"text\": \"out to another level also going through in the shop chop scotch bonnet peppers\",\n \"words\": [\n {\n \"word\": \"out\",\n \"duration\": 0.34,\n \"codes\": [\n 546,\n 416,\n 1519,\n 1673,\n 1806,\n 1015,\n 693,\n 1447,\n 9,\n 1306,\n 1485,\n 1477,\n 1178,\n 1543,\n 1830,\n 1558,\n 1801,\n 1423,\n 1487,\n 1165,\n 1743,\n 1726,\n 1772,\n 368,\n 1555\n ]\n },\n {\n \"word\": \"to\",\n \"duration\": 0.28,\n \"codes\": [\n 1823,\n 1713,\n 1734,\n 368,\n 1547,\n 1741,\n 1737,\n 1784,\n 1801,\n 1732,\n 1389,\n 994,\n 1158,\n 1278,\n 1800,\n 1658,\n 519,\n 1542,\n 1792,\n 1700,\n 1415\n ]\n },\n {\n \"word\": \"another\",\n \"duration\": 0.4,\n \"codes\": [\n 1541,\n 1824,\n 1624,\n 1757,\n 1294,\n 1734,\n 1756,\n 1821,\n 1147,\n 1663,\n 1697,\n 1156,\n 1069,\n 53,\n 1223,\n 1212,\n 1736,\n 1748,\n 1744,\n 758,\n 1494,\n 374,\n 1187,\n 1448,\n 1410,\n 1356,\n 1732,\n 1452,\n 1295,\n 1656\n ]\n },\n {\n \"word\": \"level\",\n \"duration\": 1.86,\n \"codes\": [\n 1688,\n 1527,\n 1417,\n 1486,\n 384,\n 1378,\n 1342,\n 1075,\n 1046,\n 1247,\n 1660,\n 1525,\n 719,\n 1769,\n 1628,\n 1810,\n 1078,\n 1429,\n 1483,\n 1280,\n 1814,\n 1115,\n 184,\n 1014,\n 1686,\n 1341,\n 1347,\n 1502,\n 1350,\n 1666,\n 1686,\n 1823,\n 1749,\n 1412,\n 1651,\n 1832,\n 1701,\n 1782,\n 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1662,\n 1728,\n 1016,\n 1796\n ]\n },\n {\n \"word\": \"going\",\n \"duration\": 0.26,\n \"codes\": [\n 1789,\n 1291,\n 1209,\n 828,\n 1452,\n 1749,\n 1052,\n 1460,\n 1783,\n 1656,\n 1542,\n 1281,\n 1710,\n 1716,\n 1404,\n 1734,\n 495,\n 1624,\n 1747\n ]\n },\n {\n \"word\": \"through\",\n \"duration\": 0.34,\n \"codes\": [\n 1465,\n 1664,\n 1786,\n 231,\n 1826,\n 1318,\n 1494,\n 1505,\n 1063,\n 1311,\n 1656,\n 1265,\n 1720,\n 1226,\n 940,\n 1490,\n 1447,\n 1730,\n 1348,\n 1637,\n 1118,\n 1710,\n 841,\n 795,\n 298,\n 1216\n ]\n },\n {\n \"word\": \"in\",\n \"duration\": 0.42,\n \"codes\": [\n 899,\n 1240,\n 869,\n 679,\n 1343,\n 1280,\n 1681,\n 1221,\n 1632,\n 1221,\n 1479,\n 1431,\n 1623,\n 1372,\n 1722,\n 1494,\n 1011,\n 1636,\n 957,\n 1661,\n 939,\n 1772,\n 1096,\n 1688,\n 1537,\n 1360,\n 1734,\n 1595,\n 1781,\n 1284,\n 1413\n ]\n },\n {\n \"word\": \"the\",\n \"duration\": 1.08,\n \"codes\": [\n 1701,\n 1447,\n 1328,\n 1690,\n 1281,\n 1401,\n 700,\n 1295,\n 1494,\n 1326,\n 1218,\n 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1683,\n 1779,\n 1660,\n 183,\n 1535,\n 416\n ]\n },\n {\n \"word\": \"scotch\",\n \"duration\": 0.4,\n \"codes\": [\n 1518,\n 1679,\n 0,\n 1695,\n 1682,\n 1098,\n 1764,\n 1256,\n 1808,\n 1609,\n 1745,\n 1318,\n 632,\n 1197,\n 271,\n 1683,\n 1774,\n 1824,\n 1783,\n 1671,\n 1805,\n 22,\n 631,\n 117,\n 1345,\n 800,\n 1707,\n 1466,\n 1005,\n 1462\n ]\n },\n {\n \"word\": \"bonnet\",\n \"duration\": 0.34,\n \"codes\": [\n 1677,\n 1826,\n 1277,\n 524,\n 1001,\n 789,\n 973,\n 1509,\n 1817,\n 546,\n 1260,\n 1117,\n 782,\n 142,\n 1455,\n 947,\n 1814,\n 1815,\n 0,\n 1538,\n 1766,\n 1744,\n 1824,\n 239,\n 1710\n ]\n },\n {\n \"word\": \"peppers\",\n \"duration\": 0.5,\n \"codes\": [\n 1817,\n 1287,\n 1769,\n 1309,\n 446,\n 1173,\n 1183,\n 375,\n 1342,\n 1815,\n 1382,\n 1685,\n 1797,\n 1351,\n 1798,\n 1631,\n 749,\n 1717,\n 1324,\n 1147,\n 1186,\n 955,\n 577,\n 1736,\n 827,\n 1240,\n 1484,\n 847,\n 1661,\n 1475,\n 1287,\n 1535,\n 595,\n 1286,\n 1734,\n 1256,\n 319,\n 1688\n ]\n }\n ]\n}" }, { "path": "python-wrapper/default_speakers/osagie.json", "content": "{\n \"text\": \"do Charlotte Douglas shallots be me shut up dummy Libby shallots foolish storms\",\n \"words\": [\n {\n \"word\": \"do\",\n \"duration\": 1.18,\n \"codes\": [\n 1798,\n 858,\n 1653,\n 1400,\n 1441,\n 1810,\n 1180,\n 892,\n 1487,\n 380,\n 208,\n 452,\n 181,\n 714,\n 521,\n 152,\n 1180,\n 2,\n 142,\n 756,\n 208,\n 874,\n 380,\n 565,\n 422,\n 656,\n 81,\n 860,\n 146,\n 1042,\n 1685,\n 1580,\n 50,\n 137,\n 132,\n 170,\n 1633,\n 648,\n 1819,\n 898,\n 1247,\n 1646,\n 1491,\n 438,\n 85,\n 46,\n 170,\n 664,\n 2,\n 236,\n 65,\n 100,\n 393,\n 324,\n 170,\n 1499,\n 1619,\n 519,\n 123,\n 798,\n 79,\n 1447,\n 132,\n 146,\n 779,\n 380,\n 221,\n 1588,\n 228,\n 1443,\n 152,\n 1366,\n 1441,\n 189,\n 320,\n 1387,\n 368,\n 1599,\n 295,\n 65,\n 1353,\n 13,\n 920,\n 1341,\n 55,\n 315,\n 1542,\n 315\n ]\n },\n {\n \"word\": \"charlotte\",\n \"duration\": 0.42,\n \"codes\": [\n 543,\n 769,\n 69,\n 714,\n 725,\n 212,\n 374,\n 1439,\n 25,\n 1453,\n 637,\n 291,\n 1212,\n 106,\n 1671,\n 146,\n 82,\n 1261,\n 1710,\n 686,\n 1571,\n 213,\n 298,\n 510,\n 452,\n 1396,\n 1635,\n 1760,\n 1469,\n 1793,\n 1233,\n 851\n ]\n },\n {\n \"word\": \"douglas\",\n \"duration\": 0.42,\n \"codes\": [\n 1539,\n 2,\n 679,\n 51,\n 215,\n 1068,\n 295,\n 115,\n 1150,\n 753,\n 1806,\n 287,\n 85,\n 725,\n 1312,\n 293,\n 614,\n 1610,\n 380,\n 260,\n 1014,\n 104,\n 777,\n 1697,\n 270,\n 580,\n 794,\n 1345,\n 1552,\n 7,\n 178\n ]\n },\n {\n \"word\": \"shallots\",\n \"duration\": 0.48,\n \"codes\": [\n 315,\n 290,\n 333,\n 1761,\n 412,\n 520,\n 125,\n 367,\n 1001,\n 700,\n 1258,\n 955,\n 388,\n 880,\n 324,\n 637,\n 642,\n 1723,\n 1480,\n 990,\n 507,\n 652,\n 69,\n 1670,\n 1073,\n 1433,\n 830,\n 1737,\n 1769,\n 1829,\n 1524,\n 1605,\n 1737,\n 1660,\n 1782,\n 1687,\n 1802\n ]\n },\n {\n \"word\": \"be\",\n \"duration\": 0.16,\n \"codes\": [\n 1715,\n 687,\n 1365,\n 49,\n 98,\n 357,\n 1416,\n 245,\n 1058,\n 870,\n 1689,\n 1588\n ]\n },\n {\n \"word\": \"me\",\n \"duration\": 0.36,\n \"codes\": [\n 1469,\n 1221,\n 1783,\n 127,\n 372,\n 519,\n 98,\n 50,\n 1439,\n 876,\n 362,\n 1439,\n 1506,\n 1452,\n 736,\n 1740,\n 1715,\n 1641,\n 1628,\n 1807,\n 1654,\n 1601,\n 911,\n 788,\n 1451,\n 356,\n 1450\n ]\n },\n {\n \"word\": \"shut\",\n \"duration\": 0.34,\n \"codes\": [\n 202,\n 543,\n 1527,\n 1345,\n 105,\n 721,\n 128,\n 571,\n 1180,\n 1366,\n 1187,\n 860,\n 1113,\n 1089,\n 270,\n 113,\n 525,\n 992,\n 1588,\n 975,\n 668,\n 780,\n 399,\n 233,\n 510\n ]\n },\n {\n \"word\": \"up\",\n \"duration\": 0.1,\n \"codes\": [\n 1715,\n 1833,\n 1719,\n 363,\n 1763,\n 1784,\n 1765,\n 85\n ]\n },\n {\n \"word\": \"dummy\",\n \"duration\": 0.36,\n \"codes\": [\n 101,\n 47,\n 1127,\n 205,\n 164,\n 647,\n 300,\n 737,\n 300,\n 910,\n 549,\n 1598,\n 333,\n 900,\n 1521,\n 1287,\n 917,\n 362,\n 290,\n 1353,\n 917,\n 407,\n 1588,\n 1396,\n 1415,\n 440,\n 1565\n ]\n },\n {\n \"word\": \"libby\",\n \"duration\": 0.36,\n \"codes\": [\n 935,\n 479,\n 153,\n 127,\n 162,\n 782,\n 932,\n 1023,\n 1262,\n 343,\n 1728,\n 502,\n 1401,\n 996,\n 350,\n 1445,\n 856,\n 298,\n 48,\n 1698,\n 1470,\n 1736,\n 26,\n 1342,\n 328,\n 372,\n 1451\n ]\n },\n {\n \"word\": \"shallots\",\n \"duration\": 0.4,\n \"codes\": [\n 7,\n 50,\n 519,\n 1221,\n 212,\n 238,\n 1083,\n 844,\n 333,\n 182,\n 472,\n 839,\n 609,\n 656,\n 208,\n 291,\n 1234,\n 1678,\n 1151,\n 867,\n 290,\n 546,\n 848,\n 1700,\n 1740,\n 26,\n 1617,\n 1238,\n 183,\n 1693\n ]\n },\n {\n \"word\": \"foolish\",\n \"duration\": 0.38,\n \"codes\": [\n 863,\n 176,\n 1546,\n 1470,\n 1435,\n 716,\n 1460,\n 1013,\n 217,\n 1374,\n 736,\n 91,\n 959,\n 767,\n 1678,\n 1541,\n 903,\n 362,\n 1336,\n 1345,\n 546,\n 848,\n 253,\n 335,\n 510,\n 69,\n 546,\n 1166,\n 1677\n ]\n },\n {\n \"word\": \"storms\",\n \"duration\": 0.4,\n \"codes\": [\n 939,\n 1361,\n 1719,\n 1428,\n 1691,\n 319,\n 1596,\n 236,\n 757,\n 1625,\n 123,\n 1297,\n 55,\n 132,\n 708,\n 92,\n 1344,\n 848,\n 1232,\n 518,\n 695,\n 1726,\n 1502,\n 1759,\n 363,\n 1751,\n 1524,\n 409,\n 189,\n 0\n ]\n }\n ]\n}" }, { "path": "python-wrapper/default_speakers/regina.json", "content": "{\n \"text\": \"was just like is that what is amazing to you your marriage is\",\n \"words\": [\n {\n \"word\": \"was\",\n \"duration\": 1.02,\n \"codes\": [\n 1514,\n 571,\n 892,\n 386,\n 186,\n 1403,\n 1082,\n 636,\n 851,\n 1287,\n 1678,\n 1166,\n 162,\n 1345,\n 282,\n 104,\n 1345,\n 329,\n 637,\n 844,\n 537,\n 1366,\n 537,\n 282,\n 1485,\n 537,\n 637,\n 844,\n 537,\n 1710,\n 375,\n 452,\n 1588,\n 537,\n 1382,\n 714,\n 206,\n 333,\n 330,\n 344,\n 281,\n 1523,\n 44,\n 1557,\n 315,\n 479,\n 271,\n 370,\n 110,\n 498,\n 768,\n 560,\n 579,\n 847,\n 961,\n 293,\n 1351,\n 1141,\n 138,\n 1229,\n 2,\n 847,\n 1245,\n 1345,\n 1829,\n 1811,\n 1326,\n 955,\n 1314,\n 137,\n 270,\n 1743,\n 324,\n 1389,\n 1027,\n 863\n ]\n },\n {\n \"word\": \"just\",\n \"duration\": 0.28,\n \"codes\": [\n 333,\n 38,\n 1518,\n 1296,\n 146,\n 1077,\n 1204,\n 665,\n 658,\n 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652,\n 1664,\n 6,\n 654,\n 281,\n 1578,\n 1557,\n 1346,\n 756\n ]\n },\n {\n \"word\": \"is\",\n \"duration\": 0.06,\n \"codes\": [\n 1337,\n 1662,\n 198,\n 33\n ]\n },\n {\n \"word\": \"that\",\n \"duration\": 0.12,\n \"codes\": [\n 1679,\n 236,\n 934,\n 1056,\n 208,\n 609,\n 860,\n 1318,\n 1340\n ]\n },\n {\n \"word\": \"what\",\n \"duration\": 0.14,\n \"codes\": [\n 1618,\n 806,\n 1068,\n 113,\n 1686,\n 428,\n 230,\n 409,\n 263,\n 415,\n 175\n ]\n },\n {\n \"word\": \"is\",\n \"duration\": 0.1,\n \"codes\": [\n 415,\n 1773,\n 1539,\n 124,\n 1563,\n 700,\n 579\n ]\n },\n {\n \"word\": \"amazing\",\n \"duration\": 0.34,\n \"codes\": [\n 973,\n 695,\n 1247,\n 1737,\n 1609,\n 1664,\n 1006,\n 134,\n 409,\n 416,\n 774,\n 848,\n 1542,\n 10,\n 1441,\n 1539,\n 129,\n 1698,\n 687,\n 1620,\n 1340,\n 749,\n 469,\n 1695,\n 448,\n 448\n ]\n },\n {\n \"word\": \"to\",\n \"duration\": 0.12,\n \"codes\": [\n 189,\n 198,\n 124,\n 1753,\n 510,\n 1825,\n 856,\n 1441,\n 1688\n ]\n },\n {\n \"word\": 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\"codes\": [\n 1477,\n 67,\n 113,\n 1149,\n 479,\n 901,\n 1232,\n 295,\n 9,\n 1129,\n 67,\n 1825\n ]\n },\n {\n \"word\": \"marriage\",\n \"duration\": 0.8,\n \"codes\": [\n 529,\n 697,\n 695,\n 1429,\n 282,\n 626,\n 1355,\n 192,\n 1671,\n 100,\n 95,\n 1310,\n 388,\n 1155,\n 1494,\n 104,\n 104,\n 587,\n 1156,\n 67,\n 57,\n 1437,\n 697,\n 714,\n 1221,\n 1443,\n 2,\n 1357,\n 931,\n 931,\n 1298,\n 388,\n 1136,\n 1604,\n 428,\n 1240,\n 1698,\n 65,\n 1272,\n 128,\n 755,\n 79,\n 794,\n 1698,\n 1518,\n 1546,\n 1696,\n 448,\n 233,\n 1599,\n 1732,\n 1240,\n 110,\n 775,\n 483,\n 100,\n 1075,\n 346,\n 863,\n 1498\n ]\n },\n {\n \"word\": \"is\",\n \"duration\": 0.1,\n \"codes\": [\n 631,\n 18,\n 679,\n 430,\n 176,\n 10,\n 52\n ]\n }\n ]\n}" }, { "path": "python-wrapper/default_speakers/remi.json", "content": "{\n \"text\": \"animal noral human being\",\n \"words\": [\n {\n \"word\": \"animal\",\n \"duration\": 2.79,\n \"codes\": [\n 1679,\n 1711,\n 714,\n 1588,\n 906,\n 725,\n 789,\n 456,\n 79,\n 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412,\n 828,\n 138,\n 1487,\n 128,\n 660,\n 1339,\n 882,\n 154,\n 1533,\n 47,\n 312,\n 730,\n 1087,\n 764,\n 346,\n 1394,\n 179,\n 959,\n 1344,\n 324,\n 1457,\n 388,\n 57,\n 514,\n 1323,\n 631,\n 6,\n 479,\n 815,\n 1599,\n 384,\n 952,\n 1650,\n 57,\n 314,\n 320,\n 787,\n 1488,\n 147,\n 203,\n 1078,\n 192,\n 1663,\n 236,\n 1501,\n 270,\n 1280,\n 716,\n 631,\n 1584,\n 1605,\n 1779,\n 1239,\n 363,\n 1437,\n 430,\n 1554,\n 1069,\n 189,\n 319,\n 856,\n 143\n ]\n },\n {\n \"word\": \"noral\",\n \"duration\": 0.56,\n \"codes\": [\n 1831,\n 201,\n 1674,\n 1707,\n 1807,\n 487,\n 1577,\n 1394,\n 1341,\n 412,\n 814,\n 205,\n 1633,\n 79,\n 1267,\n 1625,\n 315,\n 1649,\n 4,\n 780,\n 368,\n 592,\n 1633,\n 592,\n 1431,\n 1563,\n 599,\n 176,\n 10,\n 725,\n 1468,\n 76,\n 593,\n 714,\n 146,\n 974,\n 725,\n 549,\n 57,\n 1068,\n 1729,\n 52\n ]\n },\n {\n \"word\": \"human\",\n \"duration\": 0.82,\n \"codes\": [\n 1552,\n 233,\n 298,\n 949,\n 1636,\n 380,\n 363,\n 1520,\n 1768,\n 85,\n 483,\n 876,\n 125,\n 153,\n 564,\n 200,\n 1221,\n 803,\n 1712,\n 117,\n 804,\n 688,\n 787,\n 1345,\n 592,\n 291,\n 472,\n 158,\n 132,\n 1827,\n 617,\n 157,\n 36,\n 1186,\n 1008,\n 324,\n 961,\n 644,\n 179,\n 931,\n 1400,\n 688,\n 1015,\n 488,\n 532,\n 500,\n 952,\n 945,\n 29,\n 1497,\n 529,\n 749,\n 1733,\n 439,\n 63,\n 1773,\n 1527,\n 1622,\n 728,\n 1613,\n 1274,\n 136\n ]\n },\n {\n \"word\": \"being\",\n \"duration\": 0.54,\n \"codes\": [\n 546,\n 1287,\n 166,\n 315,\n 1678,\n 882,\n 1753,\n 1018,\n 1449,\n 1581,\n 298,\n 1710,\n 1799,\n 1772,\n 1406,\n 1538,\n 1728,\n 1657,\n 1778,\n 182,\n 921,\n 217,\n 1615,\n 133,\n 217,\n 1516,\n 1830,\n 844,\n 1584,\n 338,\n 1639,\n 644,\n 417,\n 774,\n 1724,\n 648,\n 749,\n 4,\n 315,\n 1497\n ]\n }\n ]\n}" }, { "path": "python-wrapper/default_speakers/tayo.json", "content": "{\n \"text\": \"and enjoy ourselves we need more parties let party start again now we know\",\n \"words\": [\n {\n \"word\": \"and\",\n \"duration\": 0.5,\n \"codes\": [\n 82,\n 1201,\n 329,\n 992,\n 908,\n 847,\n 925,\n 1666,\n 1057,\n 1266,\n 1448,\n 1737,\n 1251,\n 1031,\n 1759,\n 1459,\n 1094,\n 1750,\n 1739,\n 1521,\n 594,\n 1625,\n 732,\n 1326,\n 1095,\n 828,\n 239,\n 752,\n 1221,\n 1382,\n 705,\n 1716,\n 865,\n 1503,\n 478,\n 1692,\n 938\n ]\n },\n {\n \"word\": \"enjoy\",\n \"duration\": 0.4,\n \"codes\": [\n 844,\n 192,\n 737,\n 344,\n 276,\n 138,\n 48,\n 1616,\n 28,\n 1530,\n 1550,\n 1383,\n 1712,\n 69,\n 1261,\n 547,\n 249,\n 1047,\n 500,\n 182,\n 63,\n 1445,\n 935,\n 865,\n 1478,\n 1670,\n 479,\n 116,\n 1674,\n 886\n ]\n },\n {\n \"word\": \"ourselves\",\n \"duration\": 0.7,\n \"codes\": [\n 467,\n 1534,\n 901,\n 569,\n 1740,\n 882,\n 1579,\n 507,\n 276,\n 1296,\n 543,\n 399,\n 404,\n 1624,\n 1666,\n 153,\n 102,\n 1323,\n 1552,\n 65,\n 898,\n 1577,\n 757,\n 1446,\n 1022,\n 363,\n 124,\n 947,\n 1441,\n 581,\n 1677,\n 1269,\n 1525,\n 1170,\n 505,\n 1681,\n 1212,\n 1273,\n 1364,\n 1513,\n 1826,\n 1139,\n 1756,\n 639,\n 1450,\n 1810,\n 1638,\n 1644,\n 1669,\n 1519,\n 851,\n 1362,\n 1672\n ]\n },\n {\n \"word\": \"we\",\n \"duration\": 0.1,\n \"codes\": [\n 875,\n 1558,\n 1249,\n 1445,\n 181,\n 738,\n 1641\n ]\n },\n {\n \"word\": \"need\",\n \"duration\": 0.14,\n \"codes\": [\n 1603,\n 177,\n 195,\n 65,\n 1600,\n 104,\n 143,\n 1574,\n 1416,\n 160,\n 50\n ]\n },\n {\n \"word\": \"more\",\n \"duration\": 0.18,\n \"codes\": [\n 48,\n 1597,\n 39,\n 1414,\n 74,\n 1192,\n 84,\n 1345,\n 748,\n 1269,\n 1672,\n 686,\n 1820,\n 1442\n ]\n },\n {\n \"word\": \"parties\",\n \"duration\": 0.56,\n \"codes\": [\n 1640,\n 1030,\n 138,\n 147,\n 413,\n 110,\n 282,\n 1633,\n 1659,\n 1524,\n 176,\n 350,\n 137,\n 1004,\n 92,\n 1240,\n 1521,\n 1376,\n 502,\n 1558,\n 592,\n 473,\n 1021,\n 1805,\n 1346,\n 1393,\n 1759,\n 1786,\n 231,\n 1728,\n 117,\n 1366,\n 1754,\n 1073,\n 1786,\n 1354,\n 1532,\n 1572,\n 1754,\n 16,\n 257,\n 273\n ]\n },\n {\n \"word\": \"let\",\n \"duration\": 0.16,\n \"codes\": [\n 1312,\n 961,\n 372,\n 212,\n 1253,\n 115,\n 656,\n 1374,\n 78,\n 1322,\n 1284,\n 343\n ]\n },\n {\n \"word\": \"party\",\n \"duration\": 0.24,\n \"codes\": [\n 1572,\n 1662,\n 25,\n 390,\n 892,\n 212,\n 637,\n 576,\n 176,\n 1702,\n 640,\n 276,\n 52,\n 648,\n 577,\n 1240,\n 276,\n 155\n ]\n },\n {\n \"word\": \"start\",\n \"duration\": 0.3,\n \"codes\": [\n 213,\n 356,\n 1603,\n 1284,\n 1442,\n 1599,\n 705,\n 82,\n 65,\n 764,\n 349,\n 370,\n 856,\n 1524,\n 1508,\n 209,\n 495,\n 1552,\n 50,\n 1588,\n 863,\n 63\n ]\n },\n {\n \"word\": \"again\",\n \"duration\": 0.3,\n \"codes\": [\n 1267,\n 273,\n 298,\n 1409,\n 101,\n 1548,\n 733,\n 625,\n 1728,\n 1283,\n 286,\n 1645,\n 1363,\n 368,\n 153,\n 289,\n 716,\n 1756,\n 865,\n 1376,\n 688,\n 332,\n 731\n ]\n },\n {\n \"word\": \"now\",\n \"duration\": 0.44,\n \"codes\": [\n 983,\n 385,\n 1002,\n 806,\n 1798,\n 95,\n 1776,\n 825,\n 1790,\n 737,\n 1595,\n 907,\n 932,\n 1786,\n 626,\n 831,\n 1823,\n 1680,\n 1780,\n 1502,\n 1206,\n 1078,\n 47,\n 829,\n 868,\n 69,\n 277,\n 429,\n 125,\n 132,\n 14,\n 1497,\n 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466,\n 253,\n 943,\n 472,\n 215,\n 143,\n 519,\n 202,\n 1389,\n 1608,\n 714,\n 1599,\n 399,\n 944,\n 124,\n 844\n ]\n }\n ]\n}" }, { "path": "python-wrapper/default_speakers/umar.json", "content": "{\n \"text\": \"that i'd like to share with everybody in the world yes sometimes you go all the way\",\n \"words\": [\n {\n \"word\": \"that\",\n \"duration\": 0.48,\n \"codes\": [\n 519,\n 848,\n 1374,\n 416,\n 940,\n 1445,\n 416,\n 753,\n 1616,\n 774,\n 803,\n 1697,\n 1541,\n 1047,\n 200,\n 462,\n 1417,\n 1313,\n 1296,\n 184,\n 1396,\n 1568,\n 1416,\n 1444,\n 1631,\n 1463,\n 702,\n 1831,\n 1564,\n 1374,\n 1580,\n 1643,\n 1681,\n 1660,\n 1124,\n 1720\n ]\n },\n {\n \"word\": \"id\",\n \"duration\": 0.38,\n \"codes\": [\n 4,\n 705,\n 1534,\n 1290,\n 1661,\n 302,\n 1798,\n 844,\n 197,\n 1027,\n 1606,\n 903,\n 1414,\n 794,\n 871,\n 882,\n 941,\n 1310,\n 871,\n 1247,\n 1140,\n 1247,\n 718,\n 1422,\n 1509,\n 1678,\n 1093,\n 1734\n ]\n },\n {\n \"word\": \"like\",\n \"duration\": 0.18,\n \"codes\": [\n 647,\n 1824,\n 474,\n 1111,\n 599,\n 221,\n 1435,\n 822,\n 1409,\n 1717,\n 1748,\n 1550,\n 1738,\n 1717\n ]\n },\n {\n \"word\": \"to\",\n \"duration\": 0.14,\n \"codes\": [\n 1535,\n 231,\n 1794,\n 1553,\n 1351,\n 1365,\n 1296,\n 1781,\n 1599,\n 1082\n ]\n },\n {\n \"word\": \"share\",\n \"duration\": 0.18,\n \"codes\": [\n 1737,\n 0,\n 979,\n 1688,\n 546,\n 1807,\n 319,\n 252,\n 1805,\n 714,\n 580,\n 1524,\n 798,\n 1779\n ]\n },\n {\n \"word\": \"with\",\n \"duration\": 0.14,\n \"codes\": [\n 1698,\n 702,\n 966,\n 1461,\n 127,\n 1681,\n 85,\n 1741,\n 1588,\n 718\n ]\n },\n {\n \"word\": \"everybody\",\n \"duration\": 0.4,\n \"codes\": [\n 1600,\n 806,\n 1770,\n 1078,\n 1727,\n 679,\n 1569,\n 1452,\n 1685,\n 774,\n 1598,\n 1382,\n 1520,\n 1786,\n 1702,\n 1607,\n 1747,\n 828,\n 1553,\n 983,\n 1103,\n 882,\n 1427,\n 1679,\n 1613,\n 1636,\n 1433,\n 519,\n 853,\n 1451\n ]\n },\n {\n \"word\": \"in\",\n \"duration\": 0.06,\n \"codes\": [\n 1369,\n 1654,\n 1581,\n 1600,\n 1452\n ]\n },\n {\n \"word\": \"the\",\n \"duration\": 0.12,\n \"codes\": [\n 1241,\n 1769,\n 678,\n 1751,\n 1280,\n 1711,\n 1663,\n 1772,\n 1655\n ]\n },\n {\n \"word\": \"world\",\n \"duration\": 0.74,\n \"codes\": [\n 973,\n 1231,\n 1015,\n 1052,\n 1415,\n 721,\n 1822,\n 825,\n 1076,\n 1431,\n 1357,\n 1389,\n 744,\n 1263,\n 1525,\n 1794,\n 319,\n 1678,\n 1732,\n 1395,\n 1695,\n 1827,\n 1059,\n 1719,\n 1675,\n 1714,\n 1635,\n 1466,\n 1730,\n 1750,\n 1395,\n 1525,\n 1827,\n 1313,\n 1440,\n 1447,\n 1292,\n 1762,\n 1226,\n 1418,\n 1750,\n 719,\n 1549,\n 1761,\n 1459,\n 1717,\n 1800,\n 1404,\n 1702,\n 1795,\n 1711,\n 1789,\n 1808,\n 1759,\n 385,\n 415\n ]\n },\n {\n \"word\": \"yes\",\n \"duration\": 0.32,\n \"codes\": [\n 302,\n 1704,\n 485,\n 983,\n 234,\n 63,\n 462,\n 483,\n 82,\n 827,\n 999,\n 1143,\n 102,\n 1655,\n 117,\n 1619,\n 519,\n 1217,\n 1518,\n 1476,\n 333,\n 1660,\n 1238,\n 1679\n ]\n },\n {\n \"word\": \"sometimes\",\n \"duration\": 0.58,\n \"codes\": [\n 1287,\n 546,\n 1552,\n 1736,\n 1647,\n 836,\n 575,\n 354,\n 1156,\n 1264,\n 1194,\n 1761,\n 1629,\n 1452,\n 1241,\n 1394,\n 856,\n 1313,\n 1653,\n 736,\n 556,\n 1387,\n 1824,\n 966,\n 373,\n 1424,\n 1342,\n 221,\n 580,\n 1412,\n 940,\n 626,\n 1797,\n 858,\n 972,\n 1525,\n 1744,\n 738,\n 1695,\n 1542,\n 1604,\n 1394,\n 1627\n ]\n },\n {\n \"word\": \"you\",\n \"duration\": 0.12,\n \"codes\": [\n 1460,\n 546,\n 1427,\n 1451,\n 1081,\n 1760,\n 1463,\n 1628,\n 1692\n ]\n },\n {\n \"word\": \"go\",\n \"duration\": 0.26,\n \"codes\": [\n 1521,\n 1734,\n 753,\n 770,\n 1640,\n 1757,\n 297,\n 462,\n 702,\n 1826,\n 1440,\n 1828,\n 1747,\n 1651,\n 1729,\n 1087,\n 580,\n 1698,\n 1194,\n 1308\n ]\n },\n {\n \"word\": \"all\",\n \"duration\": 0.42,\n \"codes\": [\n 863,\n 610,\n 429,\n 443,\n 1087,\n 183,\n 782,\n 613,\n 222,\n 1047,\n 1492,\n 154,\n 955,\n 429,\n 443,\n 613,\n 983,\n 328,\n 382,\n 359,\n 341,\n 217,\n 456,\n 289,\n 1324,\n 714,\n 756,\n 369,\n 211,\n 127,\n 1827,\n 1563\n ]\n },\n {\n \"word\": \"the\",\n \"duration\": 0.12,\n \"codes\": [\n 1686,\n 949,\n 1296,\n 829,\n 1463,\n 1731,\n 1222,\n 1353,\n 1780\n ]\n },\n {\n \"word\": \"way\",\n \"duration\": 0.18,\n \"codes\": [\n 1263,\n 890,\n 683,\n 289,\n 217,\n 326,\n 335,\n 1059,\n 1204,\n 213,\n 1340,\n 289,\n 191\n ]\n }\n ]\n}" }, { "path": "python-wrapper/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools>=42\", \"wheel\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"yarngpt\"\nversion = \"0.1.5\"\ndescription = \"A Python wrapper for YarnGPT text-to-speech model\"\nreadme = \"README.md\"\nrequires-python = \">=3.6\"\nlicense = { file = \"LICENSE\" }\nauthors = [\n { name = \"Abayomi Olagunju\", email = \"olagunjujeremiah@gmail.com\" }\n]\nurls = { \"Homepage\" = \"https://github.com/jerryola1\" }\ndependencies = [\n \"torch\",\n \"transformers\",\n \"torchaudio\",\n \"outetts==0.2.3\",\n \"uroman\",\n \"numpy\",\n \"inflect\",\n \"IPython\",\n \"tqdm\"\n]\nclassifiers = [\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\"\n]\n\n[tool.setuptools.packages.find]\nwhere = [\".\"]\ninclude = [\"yarngpt\", \"yarngpt.*\", \"default_speakers\"]\nexclude = [\n \"models\",\n \"dist\",\n \"env\",\n \"testenv\",\n \"tests\",\n \"__pycache__\",\n \"yarngpt.egg-info\",\n \"freshenv\"\n]\n\n[tool.setuptools]\npy-modules = [\"audiotokenizer\"]\n\n[tool.setuptools.package-data]\ndefault_speakers = [\"*.json\"]\n" }, { "path": "python-wrapper/requirements.txt", "content": "torch\ntransformers\ntorchaudio\noutetts==0.2.3\nuroman\nnumpy\ninflect\nIPython\nbuild\ntqdm\n" }, { "path": "python-wrapper/yarngpt/__init__.py", "content": "from yarngpt.core import generate_speech\n\n__version__ = \"0.1.5\"\n__all__ = [\"generate_speech\"]\n" }, { "path": "python-wrapper/yarngpt/core.py", "content": "import os\nimport torch\nimport requests\nfrom transformers import AutoModelForCausalLM\nfrom audiotokenizer import AudioTokenizer\nfrom tqdm import tqdm\n\n#define model storage directory\nMODEL_DIR = os.path.expanduser(\"~/.yarngpt/models\")\nos.makedirs(MODEL_DIR, exist_ok=True)\n\n#define file paths\nCONFIG_PATH = os.path.join(MODEL_DIR, \"wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\")\nMODEL_PATH = os.path.join(MODEL_DIR, \"wavtokenizer_large_speech_320_24k.ckpt\")\n\n#urls from Hugging Face\nCONFIG_URL = \"https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\"\nMODEL_URL = \"https://huggingface.co/novateur/WavTokenizer-large-speech-75token/resolve/main/wavtokenizer_large_speech_320_24k.ckpt\"\n\ndef download_file(url, dest_path):\n \"\"\"Downloads a file with a progress bar if it doesn't already exist.\"\"\"\n if os.path.exists(dest_path):\n print(f\"{dest_path} already exists. Skipping download.\")\n return\n\n print(f\"Downloading {url} to {dest_path}...\")\n\n response = requests.get(url, stream=True)\n total_size = int(response.headers.get('content-length', 0))\n\n with open(dest_path, \"wb\") as f, tqdm(\n total=total_size, unit=\"B\", unit_scale=True, desc=os.path.basename(dest_path)\n ) as progress_bar:\n for chunk in response.iter_content(chunk_size=8192):\n f.write(chunk)\n progress_bar.update(len(chunk))\n\n print(\"Download complete.\")\n\n#ensure model files are available\ndownload_file(CONFIG_URL, CONFIG_PATH)\ndownload_file(MODEL_URL, MODEL_PATH)\n\n#list of available speakers\nAVAILABLE_SPEAKERS = [\n \"idera\", \"jude\", \"joke\", \"umar\", \"osagie\", \"onye\"\n]\n\ndef load_model_and_tokenizer():\n \"\"\"Loads the YarnGPT model and tokenizer.\"\"\"\n hf_path = \"saheedniyi/YarnGPT\"\n\n #initialize tokenizer\n audio_tokenizer = AudioTokenizer(hf_path, MODEL_PATH, CONFIG_PATH)\n\n #load model using Hugging Face's caching system\n model = AutoModelForCausalLM.from_pretrained(hf_path, torch_dtype=\"auto\")\n model = model.to(audio_tokenizer.device)\n\n return model, audio_tokenizer\n\ndef generate_speech(text, speaker=\"idera\", temperature=0.1, repetition_penalty=1.1, max_length=4000):\n \"\"\"Generate speech audio from input text using the selected speaker.\n \n This function converts text to speech using YarnGPT's text-to-speech model with\n Nigerian-accented English. It supports multiple preset voices and allows customization\n of generation parameters.\n\n Args:\n text (str): The input text to convert to speech.\n speaker (str, optional): The voice to use for speech generation.\n Must be one of: idera, jude, joke, umar, osagie, onye.\n Defaults to \"idera\".\n temperature (float, optional): Controls randomness in generation.\n Higher values (e.g., 0.8) make output more random,\n lower values (e.g., 0.1) make it more deterministic.\n Defaults to 0.1.\n repetition_penalty (float, optional): Penalizes repetition in generated speech.\n Values > 1.0 reduce repetition. Defaults to 1.1.\n max_length (int, optional): Maximum length of generated sequence.\n Longer text needs higher values. Defaults to 4000.\n\n Returns:\n torch.Tensor: A 2D tensor containing the generated audio waveform\n with shape (1, num_samples) and sample rate of 24kHz.\n\n Raises:\n ValueError: If speaker is not one of the available preset voices.\n \n Example:\n >>> from yarngpt import generate_speech\n >>> import torchaudio\n >>> \n >>> # Generate speech with default settings\n >>> audio = generate_speech(\"Hello, how are you?\")\n >>> \n >>> # Save the generated audio\n >>> torchaudio.save(\"output.wav\", audio, sample_rate=24000)\n >>> \n >>> # Use a different speaker with custom parameters\n >>> audio = generate_speech(\n ... \"This is a test.\",\n ... speaker=\"joke\",\n ... temperature=0.2,\n ... repetition_penalty=1.2\n ... )\n \"\"\"\n if speaker not in AVAILABLE_SPEAKERS:\n raise ValueError(f\"Speaker must be one of: {', '.join(AVAILABLE_SPEAKERS)}\")\n \n model, audio_tokenizer = load_model_and_tokenizer()\n prompt = audio_tokenizer.create_prompt(text, speaker)\n input_ids = audio_tokenizer.tokenize_prompt(prompt)\n \n output = model.generate(\n input_ids=input_ids,\n temperature=temperature,\n repetition_penalty=repetition_penalty,\n max_length=max_length\n )\n \n codes = audio_tokenizer.get_codes(output)\n audio = audio_tokenizer.get_audio(codes)\n \n return audio\n" }, { "path": "requirements.txt", "content": "outetts==0.2.3 \nuroman\ntorch\ntorchaudio\ntransformers==4.47.1\ninflect\n" } ]