Repository: saheedniyi02/yarngpt Branch: main Commit: 8bb0eb27d307 Files: 57 Total size: 3.2 MB Directory structure: gitextract_5ttmyi3a/ ├── README.md ├── __init__.py ├── audiotokenizer.py ├── default_speakers/ │ ├── azeez.json │ ├── chinenye.json │ ├── emma.json │ ├── idera.json │ ├── joke.json │ ├── jude.json │ ├── onye.json │ ├── osagie.json │ ├── regina.json │ ├── remi.json │ ├── saheed.json │ ├── tayo.json │ ├── umar.json │ └── zainab.json ├── default_speakers_local/ │ ├── hausa_female1.json │ ├── hausa_female2.json │ ├── hausa_male1.json │ ├── hausa_male2.json │ ├── igbo_female1.json │ ├── igbo_female2.json │ ├── igbo_male2.json │ ├── yoruba_female1.json │ ├── yoruba_female2.json │ ├── yoruba_male1.json │ ├── yoruba_male2.json │ └── yoruba_male3.json ├── notebooks/ │ ├── Merge_datasets.ipynb │ ├── Merge_datasets_local (1).ipynb │ ├── Yoruba_prepare_data_naij (2).ipynb │ ├── train_YarnGPT.ipynb │ └── train_YarnGPT_local.ipynb ├── python-wrapper/ │ ├── README.md │ ├── audiotokenizer.py │ ├── default_speakers/ │ │ ├── .ipynb_checkpoints/ │ │ │ ├── Yoruba_prepare_data_naij (2)-checkpoint.ipynb │ │ │ ├── emma-checkpoint.json │ │ │ ├── idera-checkpoint.json │ │ │ └── onye-checkpoint.json │ │ ├── Yoruba_prepare_data_naij (2).ipynb │ │ ├── chinenye.json │ │ ├── emma.json │ │ ├── idera.json │ │ ├── joke.json │ │ ├── jude.json │ │ ├── onye.json │ │ ├── osagie.json │ │ ├── regina.json │ │ ├── remi.json │ │ ├── tayo.json │ │ └── umar.json │ ├── pyproject.toml │ ├── requirements.txt │ └── yarngpt/ │ ├── __init__.py │ └── core.py └── requirements.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # YarnGPT 🎙️ ![image/png](https://github.com/saheedniyi02/yarngpt/blob/main/notebooks%2Faudio_0c026c21-f432-4d20-a86b-899a10d9ed60.webp) A text-to-speech model generating natural Nigerian-accented English speech. Built on pure language modeling without external adapters. Web Url: https://yarngpt.co/ ## Quick Start ```python !git clone https://github.com/saheedniyi02/yarngpt.git pip install outetts uroman import os import re import json import torch import inflect import random import uroman as ur import numpy as np import torchaudio import IPython from transformers import AutoModelForCausalLM, AutoTokenizer from outetts.wav_tokenizer.decoder import WavTokenizer !wget https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml !gdown 1-ASeEkrn4HY49yZWHTASgfGFNXdVnLTt from yarngpt.audiotokenizer import AudioTokenizerV2 tokenizer_path="saheedniyi/YarnGPT2" wav_tokenizer_config_path="/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml" wav_tokenizer_model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt" audio_tokenizer=AudioTokenizerV2( tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path ) model = AutoModelForCausalLM.from_pretrained(tokenizer_path,torch_dtype="auto").to(audio_tokenizer.device) #change the text text="The election was won by businessman and politician, Moshood Abiola, but Babangida annulled the results, citing concerns over national security." # change the language and voice prompt=audio_tokenizer.create_prompt(text,lang="english",speaker_name="idera") input_ids=audio_tokenizer.tokenize_prompt(prompt) output = model.generate( input_ids=input_ids, temperature=0.1, repetition_penalty=1.1, max_length=4000, #num_beams=5,# using a beam size helps for the local languages but not english ) codes=audio_tokenizer.get_codes(output) audio=audio_tokenizer.get_audio(codes) IPython.display.Audio(audio,rate=24000) torchaudio.save(f"Sample.wav", audio, sample_rate=24000) ``` ## Features - 🗣️ 12 preset voices (6 male, 6 female) - 🎯 Trained on 2000+ hours of Nigerian audio - 🔊 24kHz high-quality audio output - 🚀 Simple API for quick integration - 📝 Support for long-form text ## Available Voices - Female: zainab, idera, regina, chinenye, joke, remi - Male: jude, tayo, umar, osagie, onye, emma ## Examples Check out our [demo notebook](link-to-notebook) or listen to [sample outputs](https://huggingface.co/saheedniyi/YarnGPT/tree/main/audio). ## Model Details - Base: [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) - Training: 5 epochs on A100 GPU - Data: Nigerian movies, podcasts, and open-source audio - Architecture: Pure language modeling approach ## Limitations - English to Nigerian-accented English only - May not capture all Nigerian accent variations - Training data includes auto-generated content ## Citation ```bibtex @misc{yarngpt2025, author = {Saheed Azeez}, title = {YarnGPT: Nigerian-Accented English Text-to-Speech Model}, year = {2025}, publisher = {Hugging Face} } ``` ## License MIT ## Acknowledgments Built with [WavTokenizer](https://github.com/jishengpeng/WavTokenizer) and inspired by [OuteTTS](https://huggingface.co/OuteAI/OuteTTS-0.2-500M/). ================================================ FILE: __init__.py ================================================ ================================================ FILE: audiotokenizer.py ================================================ import os import re import json import torch import inflect import random import uroman as ur import numpy as np import torchaudio from transformers import AutoTokenizer from outetts.wav_tokenizer.decoder import WavTokenizer from outetts.wav_tokenizer.encoder.utils import convert_audio class AudioTokenizer: def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n" self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) self.bos = "<|im_start|>" self.eos = "<|im_end|>" self.input_length=0 self.special_tokens = { "audio_code": "<|{}|>", "text_start": "<|text_start|>", "text_end": "<|text_end|>", "audio_start": "<|audio_start|>", "audio_end": "<|audio_end|>", "time": "<|t_{:.2f}|>", "code_start": "<|code_start|>", "code_end": "<|code_end|>", "text_sep": "<|text_sep|>" } self.lec = inflect.engine() #self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n" #self.config_path = "/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml" #self.model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt" self.wavtokenizer = WavTokenizer.from_pretrained0802(wav_tokenizer_config_path, wav_tokenizer_model_path) self.wavtokenizer = self.wavtokenizer.to(self.device) self.BASE_DIR = os.path.dirname(__file__) self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers") self.speakers=["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye"] def get_speaker_path(self,speaker_name): return os.path.join(self.DEFAULT_SPEAKERS_DIR, f"{speaker_name}.json") def load_speaker(self, path: str): with open(path, "r") as f: return json.load(f) def load_default_speaker(self, name: str): name = name.lower().strip() speaker_path=self.get_speaker_path(name) return self.load_speaker(speaker_path) def process_text(self, text: str): text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower()) text = re.sub(r'[-_/,\.\\]', ' ', text) text = re.sub(r'[^a-z\s]', '', text) text = re.sub(r'\s+', ' ', text).strip() return text.split() def create_audio_prompt(self,words: list) -> str: prompt = [] for i in words: word = i["word"] duration = self.special_tokens["time"].format(float(i["duration"])) tokens = "".join([self.special_tokens["audio_code"].format(c) for c in i["codes"]]) prompt.append(f'{word}{duration}{self.special_tokens["code_start"]}{tokens}{self.special_tokens["code_end"]}') return "\n".join(prompt) def create_prompt(self,text,speaker_name="idera"): speaker=self.load_default_speaker(speaker_name) input_words = self.process_text(speaker["text"]) + self.process_text(text) #input_words = process_text(speaker["text"]) + input_words inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words]) prompt = self.text_prompt.format( bos=self.bos, text_start=self.special_tokens['text_start'], words=inputs_words_strings, text_end=self.special_tokens['text_end'], audio_start=self.special_tokens['audio_start'] ) prompt += self.create_audio_prompt(speaker["words"]) return prompt def tokenize_prompt(self, prompt): input_ids = self.tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ).to(self.device) self.input_length=input_ids.shape[1] return input_ids.to(self.device) def get_audio(self,discrete_code): discrete_code=torch.tensor([[discrete_code]]).to(self.device) features = self.wavtokenizer.codes_to_features(discrete_code).to(self.device) bandwidth_id = torch.tensor([0]).to(self.device) audio_out = self.wavtokenizer.decode(features, bandwidth_id=bandwidth_id) return audio_out.to("cpu") def extract_integers(self,s): # Match integers enclosed in vertical bars |integer| matches = re.findall(r'\|(-?\d+)\|', s) # Convert matches to integers return [int(match) for match in matches] def get_codes(self, output): new_output=self.tokenizer.decode(output[0][self.input_length:]) codes=self.extract_integers(new_output) return codes class AudioTokenizerForLocal(AudioTokenizer): def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,): super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path) self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n" self.special_tokens = { "audio_code": "<|{}|>", "text_start": "<|text_start|>", "text_end": "<|text_end|>", "audio_start": "<|audio_start|>", "audio_end": "<|audio_end|>", "word_start": "<|word_start|>", "word_end": "<|word_end|>", "time": "<|t_{:.2f}|>", "code_start": "<|code_start|>", "code_end": "<|code_end|>", "text_sep": "<|text_sep|>", "hausa":"<|hausa|>", "igbo":"<|igbo|>", "yoruba":"<|yoruba|>", } self.uroman = ur.Uroman() self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers_local") self.speakers = [ "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2", "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2" ] def process_text(self, text: str): text = self.uroman.romanize_string(text) text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower()) text = re.sub(r'[-_/,\.\\]', ' ', text) text = re.sub(r'[^a-z\s]', '', text) text = re.sub(r'\s+', ' ', text).strip() return text.split() def create_prompt(self,text,lang,speaker_name=None): assert lang in ["hausa","igbo","yoruba"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba']" #if no speaker if speaker_name is None: if lang=="hausa": speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"]) elif lang=="igbo": speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"]) else: speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"]) speaker=self.load_default_speaker(speaker_name) input_words = self.process_text(speaker["text"]) + self.process_text(text) #input_words = process_text(speaker["text"]) + input_words inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words]) prompt = self.text_prompt.format( bos=self.bos, text_start=self.special_tokens['text_start'], words=inputs_words_strings, text_end=self.special_tokens['text_end'], lang=self.special_tokens[lang], audio_start=self.special_tokens['audio_start'] ) prompt += self.create_audio_prompt(speaker["words"]) return prompt class AudioTokenizerV2(AudioTokenizer): def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,): super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path) self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n" self.asr_prompt="{bos}\n{code_start}{codes}{code_end}\n{asr}\n" self.special_tokens = { "audio_code": "<|{}|>", "text_start": "<|text_start|>", "text_end": "<|text_end|>", "audio_start": "<|audio_start|>", "audio_end": "<|audio_end|>", "word_start": "<|word_start|>", "word_end": "<|word_end|>", "time": "<|t_{:.2f}|>", "code_start": "<|code_start|>", "code_end": "<|code_end|>", "text_sep": "<|text_sep|>", "hausa":"<|hausa|>", "igbo":"<|igbo|>", "yoruba":"<|yoruba|>", "english":"<|english|>",#<|english|> "asr":"<|asr|>" } self.uroman = ur.Uroman() self.DEFAULT_SPEAKERS_DIR_LOCAL = os.path.join(self.BASE_DIR, "default_speakers_local") self.DEFAULT_SPEAKERS_ENG = os.path.join(self.BASE_DIR, "default_speakers") self.speakers_local = [ "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2", "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2" ] self.speakers_eng = ["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye","saheed"] self.changed_tokens=[('<|1836|>', '<|453|><|453|>'), ('<|1837|>', '<|1836|><|1836|>'), ('<|1838|>', '<|1837|><|1837|>'), ('<|1840|>', '<|244|><|167|>'), ('<|1841|>', '<|235|><|219|>'), ('<|1844|>', '<|453|><|244|>'), ('<|1845|>', '<|1838|><|1838|>')] def process_text(self, text: str): text = self.uroman.romanize_string(text) text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower()) text = re.sub(r'[-_/,\.\\]', ' ', text) text = re.sub(r'[^a-z\s]', '', text) text = re.sub(r'\s+', ' ', text).strip() return text.split() def get_speaker_path(self,speaker_name,dir): return os.path.join(dir, f"{speaker_name}.json") def load_speaker(self, path: str): with open(path, "r") as f: return json.load(f) def load_default_speaker(self, name: str,dir: str): name = name.lower().strip() speaker_path=self.get_speaker_path(name,dir) return self.load_speaker(speaker_path) def create_prompt(self,text,lang,speaker_name=None): assert lang in ["hausa","igbo","yoruba","english"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba','english']" #if no speaker dir=self.DEFAULT_SPEAKERS_DIR_LOCAL if speaker_name is None: if lang=="hausa": speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"]) elif lang=="igbo": speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"]) elif lang=="yoruba": speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"]) else: speaker_name=random.choice(self.speakers_eng) if lang=="english": dir=self.DEFAULT_SPEAKERS_ENG speaker=self.load_default_speaker(speaker_name,dir) input_words = self.process_text(speaker["text"]) + self.process_text(text) #input_words = process_text(speaker["text"]) + input_words inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words]) prompt = self.text_prompt.format( bos=self.bos, text_start=self.special_tokens['text_start'], words=inputs_words_strings, text_end=self.special_tokens['text_end'], lang=self.special_tokens[lang], audio_start=self.special_tokens['audio_start'] ) prompt += self.create_audio_prompt(speaker["words"]) return prompt def replace_tokens(text): for pair in self.changed_tokens: text=text.replace(pair[0],pair[-1]) return text def resample(self,audio: np.ndarray, sr: int, target_sr: int): audio = audio.to(dtype=torch.float32) #.clone().detach() audio = audio.unsqueeze(0) # 1 as last arg corresponds to mono audio resampled = convert_audio(audio, sr, target_sr, 1) return resampled.to(self.device ) def quantize_wavtokenizer(self, path): audio_data, sample_rate = torchaudio.load(path) audio_data=audio_data.squeeze() audio = self.resample(audio_data, sample_rate, 24000).to(self.device) if audio.ndim==3: audio=audio.squeeze(1) bandwidth_id = torch.tensor([0]).to(self.device ) _, codes = self.wavtokenizer.encode_infer(audio, bandwidth_id=bandwidth_id) codes = codes.squeeze(1).to(self.device)#+last_text_token res="" for code in codes[0].tolist(): res+=f"<|{code}|>" return res def create_asr_prompt(self,audio_path): codes=self.quantize_wavtokenizer(audio_path) prompt = self.asr_prompt.format( bos=self.bos, code_start=self.special_tokens['code_start'], codes=codes, code_end=self.special_tokens['code_end'], asr=self.special_tokens["asr"], ) return prompt def get_asr_results(self,output): res="" for text in self.tokenizer.decode(output[0]).split("<|text_start|>")[-1].split("<|text_end|>")[0].split("\n"): res+=text.split("<|word_start|>")[-1].split("<|word_end|>")[0] res+=" " return res.strip() ================================================ FILE: default_speakers/azeez.json ================================================ { "text": "Hello! My name is Saheed azeez and I am testing the audio feature", "words": [ { "word": "hello", "duration": 1.22, "codes": [ 219, 244, 244, 167, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 453, 244, 219, 237, 864, 1041, 1048, 1372, 1780, 1554, 1024, 702, 1814, 1754, 1315, 1697, 1719, 1682, 307, 621, 901, 355, 783, 1726, 353, 1416, 729, 803, 1494, 353, 876, 1818, 932, 1068, 1813, 875, 1774, 766, 1453, 1466, 792, 1388, 1495, 1236, 1462, 431, 1025, 1429, 1128, 1236, 1483, 1305, 1352, 1681, 5, 1758, 1481, 1339 ] }, { "word": "my", "duration": 0.18, "codes": [ 1333, 1339, 1388, 1373, 974, 723, 1776, 1001, 1160, 1769, 1048, 1646, 1321, 912 ] }, { "word": "name", "duration": 0.2, "codes": [ 1596, 325, 876, 1303, 973, 1707, 1332, 1300, 145, 1136, 1266, 1353, 845, 913, 989 ] }, { "word": "is", "duration": 0.12, "codes": [ 1257, 1372, 1617, 1800, 1568, 1679, 1798, 1476, 1759 ] }, { "word": "saheed", "duration": 0.5, "codes": [ 1807, 1354, 1737, 1738, 1060, 1122, 1195, 1275, 1129, 1473, 688, 1675, 1724, 1392, 1146, 1605, 1784, 1476, 1454, 1743, 1824, 706, 1706, 669, 91, 1079, 1456, 1645, 1041, 1687, 1425, 1205, 830, 1525, 1007, 1291, 723 ] }, { "word": "azeez", "duration": 0.48, "codes": [ 829, 926, 1438, 1124, 1282, 1745, 1019, 1430, 1657, 1715, 1637, 1653, 1713, 1370, 1534, 1410, 1767, 814, 22, 1703, 1534, 1797, 1488, 1812, 1637, 1791, 1720, 1677, 1807, 1459, 1779, 1767, 1145, 1239, 1622, 1264 ] }, { "word": "and", "duration": 0.24, "codes": [ 1780, 1291, 1174, 1435, 1494, 1807, 662, 1760, 1694, 363, 1225, 1775, 1264, 1455, 1014, 1758, 1620, 1013 ] }, { "word": "i", "duration": 0.06, "codes": [ 1823, 1295, 1397, 1108, 1275 ] }, { "word": "am", "duration": 0.14, "codes": [ 1129, 1697, 835, 1589, 1719, 1534, 1495, 1025, 1405, 766 ] }, { "word": "testing", "duration": 0.42, "codes": [ 196, 1118, 761, 1314, 1770, 1138, 1429, 728, 1497, 1792, 1049, 1430, 1062, 1788, 1354, 1555, 1735, 1728, 954, 1754, 343, 1418, 636, 1501, 1301, 901, 763, 1620, 1687, 177, 1706, 325 ] }, { "word": "the", "duration": 0.14, "codes": [ 810, 1421, 1404, 1093, 781, 752, 1780, 1749, 850, 1435 ] }, { "word": "audio", "duration": 0.3, "codes": [ 1792, 1381, 1309, 1472, 1449, 1785, 114, 601, 866, 1764, 1212, 1453, 1152, 1777, 853, 1735, 1052, 355, 1421, 1605, 1761, 1664, 540 ] }, { "word": "feature", "duration": 0.4, "codes": [ 1682, 1442, 1819, 1818, 710, 1776, 1205, 646, 1688, 1572, 875, 1367, 476, 1285, 460, 342, 1784, 28, 1621, 1745, 1462, 988, 1780, 1697, 1249, 1348, 1120, 1590, 803, 1205 ] } ] } ================================================ FILE: default_speakers/chinenye.json ================================================ { "text": "and once I got that out of the way", "words": [ { "word": "and", "duration": 1.18, "codes": [ 1073, 1804, 1510, 1562, 377, 1287, 1615, 175, 631, 1702, 1700, 1590, 1158, 1676, 758, 1727, 1548, 1464, 1605, 1469, 1291, 1755, 1656, 1323, 1372, 269, 1252, 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] } ================================================ FILE: default_speakers/jude.json ================================================ { "text": "know what I'm saying what I'm saying is that if you say", "words": [ { "word": "know", "duration": 0.44, "codes": [ 1824, 1820, 1743, 1819, 1171, 1796, 1613, 1126, 1500, 1346, 1429, 1810, 1655, 1462, 1780, 1812, 1518, 1431, 741, 1206, 1325, 1392, 920, 409, 4, 1270, 416, 1759, 1141, 708, 1022, 1769, 1384 ] }, { "word": "what", "duration": 0.12, "codes": [ 607, 787, 48, 1350, 1340, 297, 364, 825, 1775 ] }, { "word": "im", "duration": 0.1, "codes": [ 1668, 1311, 1651, 1048, 176, 430, 333 ] }, { "word": "saying", "duration": 0.56, "codes": [ 822, 648, 1568, 1660, 1071, 1399, 890, 1396, 1381, 1818, 124, 1623, 361, 1588, 1688, 1280, 1805, 1659, 1605, 1412, 1672, 1752, 1741, 1514, 1817, 1796, 1763, 1790, 1595, 1788, 1823, 758, 1466, 1802, 1788, 1649, 1614, 1751, 1718, 1585, 1637, 1773 ] }, { "word": "what", "duration": 0.12, "codes": [ 1666, 1680, 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================================================ { "text": "out to another level also going through in the shop chop scotch bonnet peppers", "words": [ { "word": "out", "duration": 0.34, "codes": [ 546, 416, 1519, 1673, 1806, 1015, 693, 1447, 9, 1306, 1485, 1477, 1178, 1543, 1830, 1558, 1801, 1423, 1487, 1165, 1743, 1726, 1772, 368, 1555 ] }, { "word": "to", "duration": 0.28, "codes": [ 1823, 1713, 1734, 368, 1547, 1741, 1737, 1784, 1801, 1732, 1389, 994, 1158, 1278, 1800, 1658, 519, 1542, 1792, 1700, 1415 ] }, { "word": "another", "duration": 0.4, "codes": [ 1541, 1824, 1624, 1757, 1294, 1734, 1756, 1821, 1147, 1663, 1697, 1156, 1069, 53, 1223, 1212, 1736, 1748, 1744, 758, 1494, 374, 1187, 1448, 1410, 1356, 1732, 1452, 1295, 1656 ] }, { "word": "level", "duration": 1.86, "codes": [ 1688, 1527, 1417, 1486, 384, 1378, 1342, 1075, 1046, 1247, 1660, 1525, 719, 1769, 1628, 1810, 1078, 1429, 1483, 1280, 1814, 1115, 184, 1014, 1686, 1341, 1347, 1502, 1350, 1666, 1686, 1823, 1749, 1412, 1651, 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================================================ FILE: default_speakers/osagie.json ================================================ { "text": "do Charlotte Douglas shallots be me shut up dummy Libby shallots foolish storms", "words": [ { "word": "do", "duration": 1.18, "codes": [ 1798, 858, 1653, 1400, 1441, 1810, 1180, 892, 1487, 380, 208, 452, 181, 714, 521, 152, 1180, 2, 142, 756, 208, 874, 380, 565, 422, 656, 81, 860, 146, 1042, 1685, 1580, 50, 137, 132, 170, 1633, 648, 1819, 898, 1247, 1646, 1491, 438, 85, 46, 170, 664, 2, 236, 65, 100, 393, 324, 170, 1499, 1619, 519, 123, 798, 79, 1447, 132, 146, 779, 380, 221, 1588, 228, 1443, 152, 1366, 1441, 189, 320, 1387, 368, 1599, 295, 65, 1353, 13, 920, 1341, 55, 315, 1542, 315 ] }, { "word": "charlotte", "duration": 0.42, "codes": [ 543, 769, 69, 714, 725, 212, 374, 1439, 25, 1453, 637, 291, 1212, 106, 1671, 146, 82, 1261, 1710, 686, 1571, 213, 298, 510, 452, 1396, 1635, 1760, 1469, 1793, 1233, 851 ] }, { "word": "douglas", "duration": 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================================================ FILE: default_speakers_local/igbo_female1.json ================================================ { "text": "Codeine na-agba ah\u1ee5 \u1ecbnweta.", "words": [ { "word": "codeine", "duration": "0.68", "codes": [ 165, 336, 1359, 661, 199, 379, 585, 1742, 210, 303, 388, 412, 1772, 794, 1607, 467, 622, 201, 575, 447, 319, 352, 234, 1797, 405, 1703, 1831, 1163, 1826, 1152, 563, 696, 1284, 157, 100, 402, 315, 1036, 1298, 592, 1177, 665, 7, 794, 509, 192, 1092, 821, 1022, 834, 132 ] }, { "word": "na", "duration": "0.20", "codes": [ 1764, 1340, 1394, 1341, 146, 303, 1102, 172, 366, 1263, 708, 164, 836, 1424, 81 ] }, { "word": "agba", "duration": "0.76", "codes": [ 994, 841, 816, 744, 1743, 1051, 1023, 1556, 331, 1706, 160, 160, 403, 142, 565, 723, 140, 874, 339, 186, 1229, 309, 461, 1015, 81, 297, 1206, 1041, 585, 960, 1007, 223, 578, 1142, 242, 1215, 261, 857, 1390, 334, 837, 735, 334, 649, 563, 544, 672, 316, 544, 630, 337, 601, 978, 956, 642, 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1200, 1217, 1178, 293, 30, 36 ] } ] } ================================================ FILE: notebooks/Merge_datasets.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "mKb-4Hv4xNpF", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "8f45fbf9-5e31-4995-a18b-b5d2b9a9e9f5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting datasets\n", " Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n", "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n", "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n", " Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n", 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installation: fsspec 2024.10.0\n", " Uninstalling fsspec-2024.10.0:\n", " Successfully uninstalled fsspec-2024.10.0\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", "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\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" ] } ], "source": [ "pip install datasets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-Oz-lmmExH_F" }, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import huggingface_hub\n", "import datasets\n", "from datasets import load_dataset, load_from_disk,concatenate_datasets" ] }, { "cell_type": "code", "source": [ "all_df=[]\n", "for df_path in os.listdir(\"/content/drive/MyDrive/Tokenized\"):\n", " if ('.gsheet' not in df_path) and ((\"yt\" in df_path) or (\"mv\" in df_path)):\n", " print(df_path)\n", " all_df.append( pd.read_csv(f\"/content/drive/MyDrive/Tokenized/{df_path}\"))\n", " #print(df_path)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ACU3ZWjAfTsI", "outputId": "d57c0bd1-6161-4ef3-f722-480e1f40af05" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "tokenized_yt0.csv\n", "tokenized_mv0.csv\n", "tokenized_yt1.csv\n", "tokenized_mv1.csv\n", "tokenized_yt2.csv\n", "tokenized_mv2.csv\n", "tokenized_yt3.csv\n", "tokenized_mv3.csv\n", "tokenized_yt4.csv\n", "tokenized_mv4.csv\n", "tokenized_yt5.csv\n", "tokenized_mv5.csv\n", "tokenized_yt6.csv\n", "tokenized_mv6.csv\n", "tokenized_yt7.csv\n", "tokenized_mv7.csv\n", "tokenized_yt8.csv\n", "tokenized_mv8.csv\n", "tokenized_yt9.csv\n", "tokenized_mv9.csv\n", "tokenized_yt10.csv\n", "tokenized_mv10.csv\n", "tokenized_yt11.csv\n", "tokenized_mv11.csv\n", "tokenized_yt12.csv\n", "tokenized_mv12.csv\n", "tokenized_yt13.csv\n", "tokenized_mv13.csv\n", "tokenized_yt14.csv\n", "tokenized_mv14.csv\n", "tokenized_yt15.csv\n", "tokenized_mv15.csv\n", "tokenized_yt16.csv\n", "tokenized_mv16.csv\n", "tokenized_yt17.csv\n", "tokenized_mv17.csv\n", "tokenized_yt18.csv\n", "tokenized_mv18.csv\n", "tokenized_yt19.csv\n", "tokenized_mv19.csv\n", "tokenized_yt20.csv\n", "tokenized_mv20.csv\n", "tokenized_yt21.csv\n", "tokenized_mv21.csv\n", "tokenized_yt22.csv\n", "tokenized_mv22.csv\n", "tokenized_yt23.csv\n", "tokenized_mv23.csv\n", "tokenized_yt24.csv\n", "tokenized_mv24.csv\n", "tokenized_yt25.csv\n", "tokenized_mv25.csv\n", "tokenized_yt26.csv\n", "tokenized_mv26.csv\n", "tokenized_mv27.csv\n", "tokenized_yt27.csv\n", "tokenized_mv28.csv\n", "tokenized_yt28.csv\n", "tokenized_mv29.csv\n", "tokenized_yt29.csv\n", "tokenized_mv30.csv\n", "tokenized_yt30.csv\n", "tokenized_mv31.csv\n", "tokenized_yt31.csv\n", "tokenized_mv32.csv\n", "tokenized_yt32.csv\n", "tokenized_mv33.csv\n", "tokenized_yt33.csv\n", "tokenized_mv34.csv\n", "tokenized_yt34.csv\n", "tokenized_mv35.csv\n", "tokenized_mv37.csv\n", "tokenized_mv38.csv\n", "tokenized_yt37.csv\n", "tokenized_mv39.csv\n", "tokenized_yt38.csv\n", "tokenized_mv40.csv\n", "tokenized_yt39.csv\n", "tokenized_mv41.csv\n", "tokenized_yt40.csv\n", "tokenized_mv42.csv\n", "tokenized_yt41.csv\n", "tokenized_mv43.csv\n", "tokenized_yt42.csv\n", "tokenized_mv44.csv\n", "tokenized_yt43.csv\n", "tokenized_mv45.csv\n", "tokenized_mv46.csv\n", "tokenized_yt44.csv\n", "tokenized_mv47.csv\n", "tokenized_yt45.csv\n", "tokenized_mv48.csv\n", "tokenized_yt46.csv\n", "tokenized_mv49.csv\n", "tokenized_yt47.csv\n", "tokenized_mv50.csv\n", "tokenized_yt48.csv\n", "tokenized_mv51.csv\n", "tokenized_mv70.csv\n", "tokenized_yt70.csv\n", "tokenized_mv71.csv\n", "tokenized_mv72.csv\n", "tokenized_yt71.csv\n", "tokenized_mv73.csv\n", "tokenized_yt72.csv\n", "tokenized_mv74.csv\n", "tokenized_yt73.csv\n", "tokenized_mv75.csv\n", "tokenized_yt74.csv\n", "tokenized_mv76.csv\n", "tokenized_yt75.csv\n", "tokenized_mv77.csv\n", "tokenized_yt76.csv\n", "tokenized_mv78.csv\n", "tokenized_yt77.csv\n", "tokenized_mv79.csv\n", "tokenized_yt78.csv\n", "tokenized_mv80.csv\n", "tokenized_mv81.csv\n", "tokenized_yt79.csv\n", "tokenized_mv82.csv\n", "tokenized_yt80.csv\n", "tokenized_mv83.csv\n", "tokenized_yt81.csv\n", "tokenized_mv84.csv\n", "tokenized_yt82.csv\n", "tokenized_mv85.csv\n", "tokenized_yt83.csv\n", "tokenized_mv86.csv\n", "tokenized_yt84.csv\n", "tokenized_mv87.csv\n", "tokenized_yt85.csv\n", "tokenized_mv88.csv\n", "tokenized_yt86.csv\n", "tokenized_mv89.csv\n", "tokenized_mv90.csv\n", "tokenized_yt87.csv\n", "tokenized_mv91.csv\n", "tokenized_yt88.csv\n", "tokenized_mv92.csv\n", "tokenized_yt89.csv\n", "tokenized_mv93.csv\n", "tokenized_yt90.csv\n", "tokenized_mv94.csv\n", "tokenized_mv95.csv\n", "tokenized_yt91.csv\n", "tokenized_mv96.csv\n", "tokenized_yt92.csv\n", "tokenized_mv97.csv\n", "tokenized_yt93.csv\n", "tokenized_mv98.csv\n", "tokenized_yt94.csv\n", "tokenized_mv99.csv\n", "tokenized_mv100.csv\n", "tokenized_yt95.csv\n", "tokenized_mv101.csv\n", "tokenized_yt96.csv\n", "tokenized_mv102.csv\n", "tokenized_yt97.csv\n", "tokenized_mv103.csv\n", "tokenized_yt98.csv\n", "tokenized_mv104.csv\n", "tokenized_mv105.csv\n", "tokenized_yt99.csv\n", "tokenized_mv106.csv\n", "tokenized_yt100.csv\n", "tokenized_mv107.csv\n", "tokenized_yt101.csv\n", "tokenized_mv108.csv\n", "tokenized_yt102.csv\n", "tokenized_mv109.csv\n", "tokenized_mv110.csv\n", "tokenized_yt103.csv\n", "tokenized_mv112.csv\n", "tokenized_yt104.csv\n", "tokenized_yt105.csv\n", "tokenized_yt106.csv\n", "tokenized_yt107.csv\n", "tokenized_yt108.csv\n", "tokenized_yt109.csv\n", "tokenized_yt110.csv\n", "tokenized_yt111.csv\n", "tokenized_yt112.csv\n", "tokenized_yt113.csv\n", "tokenized_yt114.csv\n", "tokenized_yt115.csv\n", "tokenized_yt116.csv\n", "tokenized_yt117.csv\n", "tokenized_yt118.csv\n", "tokenized_yt119.csv\n", "tokenized_yt120.csv\n", "tokenized_yt121.csv\n", "tokenized_yt122.csv\n", "tokenized_yt123.csv\n", "tokenized_yt124.csv\n", "tokenized_yt125.csv\n", "tokenized_yt126.csv\n", "tokenized_yt127.csv\n", "tokenized_yt128.csv\n", "tokenized_yt129.csv\n", "tokenized_yt130.csv\n", "tokenized_yt131.csv\n", "tokenized_yt132.csv\n", "tokenized_yt133.csv\n", "tokenized_yt134.csv\n", "tokenized_yt135.csv\n", "tokenized_yt136.csv\n", "tokenized_yt137.csv\n", "tokenized_yt138.csv\n", "tokenized_yt139.csv\n", "tokenized_yt140.csv\n", "tokenized_yt141.csv\n", "tokenized_yt142.csv\n", "tokenized_yt143.csv\n", "tokenized_yt144.csv\n", "tokenized_yt145.csv\n", "tokenized_yt146.csv\n", "tokenized_yt147.csv\n", "tokenized_yt148.csv\n", "tokenized_yt149.csv\n", "tokenized_yt150.csv\n", "tokenized_yt151.csv\n", "tokenized_yt152.csv\n", "tokenized_yt153.csv\n", "tokenized_yt154.csv\n", "tokenized_yt155.csv\n", "tokenized_yt156.csv\n", "tokenized_yt157.csv\n", "tokenized_yt158.csv\n", "tokenized_yt159.csv\n", "tokenized_yt160.csv\n", "tokenized_yt161.csv\n", "tokenized_yt162.csv\n", "tokenized_yt163.csv\n", "tokenized_yt164.csv\n", "tokenized_yt165.csv\n", "tokenized_yt166.csv\n", "tokenized_yt167.csv\n", "tokenized_yt168.csv\n", "tokenized_yt169.csv\n", "tokenized_yt170.csv\n", "tokenized_yt171.csv\n", "tokenized_yt172.csv\n", "tokenized_yt173.csv\n", "tokenized_yt174.csv\n", "tokenized_yt175.csv\n", "tokenized_yt176.csv\n", "tokenized_yt177.csv\n", "tokenized_yt178.csv\n", "tokenized_yt179.csv\n", "tokenized_yt180.csv\n", "tokenized_yt181.csv\n", "tokenized_yt182.csv\n", "tokenized_yt183.csv\n", "tokenized_yt184.csv\n", "tokenized_yt185.csv\n", "tokenized_yt186.csv\n", "tokenized_yt187.csv\n", "tokenized_yt188.csv\n", "tokenized_yt189.csv\n", "tokenized_yt190.csv\n", "tokenized_yt191.csv\n", "tokenized_yt192.csv\n", "tokenized_yt193.csv\n", "tokenized_yt194.csv\n", "tokenized_yt195.csv\n", "tokenized_yt196.csv\n", "tokenized_yt197.csv\n", "tokenized_yt198.csv\n", "tokenized_yt199.csv\n", "tokenized_yt200.csv\n", "tokenized_yt201.csv\n", "tokenized_yt202.csv\n", "tokenized_yt203.csv\n", "tokenized_yt204.csv\n", "tokenized_yt205.csv\n", "tokenized_yt206.csv\n", "tokenized_yt210.csv\n", "tokenized_yt211.csv\n", "tokenized_yt212.csv\n", "tokenized_yt213.csv\n", "tokenized_yt214.csv\n", "tokenized_yt215.csv\n", "tokenized_yt216.csv\n", "tokenized_yt217.csv\n", "tokenized_yt218.csv\n", "tokenized_yt219.csv\n", "tokenized_yt220.csv\n", "tokenized_yt221.csv\n", "tokenized_yt222.csv\n", "tokenized_yt223.csv\n", "tokenized_yt224.csv\n", "tokenized_yt225.csv\n", "tokenized_yt226.csv\n", "tokenized_yt227.csv\n", "tokenized_yt228.csv\n", "tokenized_yt230.csv\n", "tokenized_yt1101.csv\n" ] } ] }, { "cell_type": "code", "source": [ "train_data=pd.concat(all_df)" ], "metadata": { "id": "9f8aFlgOfb6c" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 461 }, "id": "Hsq8k7WogC_j", "outputId": "be6ce9a2-b1a0-4a7b-9251-40bc6e1e538f" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Unnamed: 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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "train_data" } }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "train_data.to_csv(\"/content/drive/MyDrive/Tokenized2/all_data.csv\")" ], "metadata": { "id": "S0011JRDtLO2" }, "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: notebooks/Merge_datasets_local (1).ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "mKb-4Hv4xNpF" }, "outputs": [], "source": [ "pip install datasets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1QHW2w8cdupP" }, "outputs": [], "source": [ "import huggingface_hub" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-Oz-lmmExH_F" }, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import huggingface_hub\n", "import datasets\n", "from datasets import load_dataset, load_from_disk,concatenate_datasets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NfVKJ5xgdyc1" }, "outputs": [], "source": [ "huggingface_hub.login()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "llvhheVWjDwi" }, "outputs": [], "source": [ "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "checkpoint=\"saheedniyi/YarnGPT\"\n", "#checkpoint=\"saheedniyi/public_extra2\"#device = \"cuda\" # for GPU usage or \"cpu\" for CPU usage\n", "tokenizer = AutoTokenizer.from_pretrained(checkpoint)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sfVhIZEpisZg" }, "outputs": [], "source": [ "def token_length(prompt):\n", " return len(tokenizer(prompt)[\"input_ids\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ACU3ZWjAfTsI" }, "outputs": [], "source": [ "all_df=[]\n", "for df_path in os.listdir(\"/content/drive/MyDrive/naij_tokenized\"):\n", " if ('.gsheet' not in df_path):\n", " df=pd.read_csv(f\"/content/drive/MyDrive/naij_tokenized/{df_path}\")\n", " df[\"length\"]=df[\"tts\"].apply(token_length)\n", " print(df_path)\n", " all_df.append(df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9f8aFlgOfb6c" }, "outputs": [], "source": [ "train_data=pd.concat(all_df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Hsq8k7WogC_j" }, "outputs": [], "source": [ "train_data_1=train_data.drop_duplicates(\"tts\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dVYrYSISn9hE" }, "outputs": [], "source": [ "train_data_1.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bec2mRTdoAf_" }, "outputs": [], "source": [ "train_data.shape" ] }, { "cell_type": "code", "source": [ "train_data_1.drop(\"stt\",axis=1, inplace=True)" ], "metadata": { "id": "pythz_XcgF9t" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_data_1.drop([\"Unnamed: 0\",\"__index_level_0__\"],axis=1, inplace=True)" ], "metadata": { "id": "9yebXiwvgZMa" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def replace_text(txt):\n", " txt=txt.replace(\"<|hausa|\\n\",\"<|hausa|>\\n\")\n", " txt=txt.replace(\"<|igbo|\\n\",\"<|igbo|>\\n\")\n", " txt=txt.replace(\"<|yoruba|\\n\",\"<|yoruba|>\\n\")#hausa\":\"<|hausa|\",\n", " txt=txt.replace(\"\\n<|tts|>\",\"\")\n", " return txt" ], "metadata": { "id": "kUvi1ItQg6HT" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TgWv4W8Wiwd1" }, "outputs": [], "source": [ "train_data_1=train_data_1[train_data_1[\"tts\"]!=\"An error occurred\"]" ] }, { "cell_type": "code", "source": [ "train_data_1[\"tts\"]=train_data_1[\"tts\"].apply(replace_text)" ], "metadata": { "id": "CP7bVwRqh_qH" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_data_1.shape" ], "metadata": { "id": "YMk40ZsIrW44" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_data_1" ], "metadata": { "id": "RVD3f0frrcfi" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_data_1=train_data_1[train_data_1[\"length\"]<4000]" ], "metadata": { "id": "utJLIf1orhnE" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_data_1.to_csv(\"/content/drive/MyDrive/naij_tokenized/final_all_lang.csv\")" ], "metadata": { "id": "BOynFjQlruWJ" }, "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: notebooks/Yoruba_prepare_data_naij (2).ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Rxa73RyKnhy3", "outputId": "aa525021-8667-4b2a-b879-f843eee12d7c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting outetts\n", " Downloading outetts-0.2.3-py3-none-any.whl.metadata (10 kB)\n", "Collecting uroman\n", " Downloading uroman-1.3.1.1-py3-none-any.whl.metadata (18 kB)\n", "Collecting noisereduce\n", " Downloading noisereduce-3.0.3-py3-none-any.whl.metadata (14 kB)\n", "Collecting mecab-python3\n", " Downloading mecab_python3-1.0.10-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.2 kB)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from outetts) (1.13.1)\n", "Requirement already 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for encodec (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for encodec: filename=encodec-0.1.1-py3-none-any.whl size=45760 sha256=451b0ff87f503b1e3e80ee75873ae179f23b53b055ffcac6e5414d3bdf11dad3\n", " Stored in directory: /root/.cache/pip/wheels/fc/36/cb/81af8b985a5f5e0815312d5e52b41263237af07b977e6bcbf3\n" ] } ], "source": [ "pip install outetts uroman noisereduce mecab-python3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HgJjekSOT8iX" }, "outputs": [], "source": [ "!pip install datasets triton snac wandb accelerate torchdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m4uPM3IpnsEo" }, "outputs": [], "source": [ "from outetts.wav_tokenizer.decoder import WavTokenizer\n", "from outetts.wav_tokenizer.encoder.utils import convert_audio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "543a-ZmC7xjE" }, "outputs": [], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EVyBedbQUM3F" }, "outputs": [], "source": [ "import torch\n", "import time\n", "import numpy as np\n", "import torchaudio\n", "from snac import SNAC\n", "from tqdm import tqdm\n", "import huggingface_hub\n", "import shutil\n", "import soundfile as sf\n", "from torch.utils.data import DataLoader, Dataset\n", "from transformers import AdamW, get_linear_schedule_with_warmup\n", "from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z8LFkziTgFRf" }, "outputs": [], "source": [ "import torchaudio\n", "import torch\n", "import torchaudio.functional as F\n", "import inflect\n", "import re\n", "import uroman as ur" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-wARjdSEUdjy" }, "outputs": [], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IYyt-dhuWx9q" }, "outputs": [], "source": [ "config_path = \"/content/drive/MyDrive/audio_datasets/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\"\n", "model_path = \"/content/drive/MyDrive/audio_datasets/wavtokenizer_large_speech_320_24k.ckpt\"#\"/content/wavtokenizer_medium_speech_320_24k_v2.ckpt\"\n", "wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)\n", "wavtokenizer = wavtokenizer.to(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TrfYeoWNV6T9" }, "outputs": [], "source": [ "class CTCForcedAlignment:\n", "\n", " def __init__(self, device: str = None):\n", " self.device = torch.device(device if device is not None else \"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " bundle = torchaudio.pipelines.MMS_FA\n", " self.sample_rate = bundle.sample_rate\n", " self.model = bundle.get_model(with_star=False).to(self.device)\n", " self.LABELS = bundle.get_labels(star=None)\n", " self.DICTIONARY = bundle.get_dict(star=None)\n", " self.lec = inflect.engine()\n", " self.uroman = ur.Uroman()\n", " #self.wakati = MeCab.Tagger(\"-Owakati\")\n", " #self.wakati_use = [\"ja\", \"zh\", \"ko\"]\n", " #self.languages = languages\n", "\n", " def process_text(self, text: str):\n", " #if language not in self.languages:\n", " # raise ValueError(f\"Language {language} not supported, supported languages are {self.languages}\")\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 _unflatten(self, list_, lengths):\n", " assert len(list_) == sum(lengths)\n", " i = 0\n", " ret = []\n", " for l in lengths:\n", " ret.append(list_[i : i + l])\n", " i += l\n", " return ret\n", "\n", " def get_word(self, waveform, spans, num_frames, transcript):\n", " ratio = waveform.size(1) / num_frames\n", " x0 = int(ratio * spans[0].start)\n", " x1 = int(ratio * spans[-1].end)\n", " return {\"x0\": x0, \"x1\": x1, \"word\": transcript}\n", "\n", " def _extract_world_level(self, aligned_tokens, alignment_scores, transcript):\n", " token_spans = F.merge_tokens(aligned_tokens, alignment_scores)\n", " word_spans = self._unflatten(token_spans, [len(word) for word in transcript])\n", " return word_spans\n", "\n", " def _align(self, emission, tokens):\n", " targets = torch.tensor([tokens], dtype=torch.int32, device=torch.device(\"cpu\"))\n", " alignments, scores = F.forced_align(emission.cpu(), targets, blank=0)\n", " alignments, scores = alignments[0], scores[0]\n", " scores = scores.exp()\n", " return alignments, scores\n", "\n", " def align(self, waveform,sr, transcript):\n", " #waveform, sr = torchaudio.load(audio)\n", " #waveform = torch.tensor(waveform)\n", " all_codes=quantize_wavtokenizer_ctc(waveform,sampling_rate=sr)\n", " if waveform.shape[0] > 1:\n", " waveform = waveform.mean(dim=0, keepdim=True)\n", " waveform = waveform.float()\n", " #print(waveform.shape)\n", " #print(sr)\n", " waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=self.sample_rate)\n", " transcript = self.process_text(transcript)\n", "\n", " with torch.inference_mode():\n", " emission, _ = self.model(waveform.to(self.device))\n", "\n", " tokenized_transcript = [self.DICTIONARY[c] for word in transcript for c in word]\n", " alignments, scores = self._align(emission, tokenized_transcript)\n", " word_spans = self._extract_world_level(alignments, scores, transcript)\n", " num_frames = emission.size(1)\n", "\n", " outputs = [\n", " self.get_word(waveform, word_spans[i], num_frames, transcript[i])\n", " for i in range(len(word_spans))\n", " ]\n", " #codes=quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000):\n", " #audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", "\n", " outputs[0][\"x0\"] = 0\n", " #print(waveform.shape)\n", " #print(self.sample_rate)\n", " for i in range(len(outputs)):\n", " output = outputs[i]\n", " x0 = output[\"x0\"]\n", "\n", " if i == len(outputs) - 1:\n", " x1 = output[\"x1\"]\n", " else:\n", " x1 = outputs[i + 1][\"x0\"]\n", " outputs[i][\"audio\"] = waveform[:, x0:x1]\n", " outputs[i][\"duration\"]=len(outputs[i][\"audio\"][0])/self.sample_rate\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", " #convert waveform to codes\n", " #duration Add audio\n", " return outputs\n", "\n", " def free(self):\n", " del self.model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CouG9BMIV6-K" }, "outputs": [], "source": [ "ctc = CTCForcedAlignment(\"cuda\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "68rBtr5GUcF2" }, "outputs": [], "source": [ "ctc.DICTIONARY" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "275g7SweCKAe" }, "outputs": [], "source": [ "def resample(audio: np.ndarray, sr: int, target_sr: int):\n", "\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(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "N85dYwCmWZG8" }, "outputs": [], "source": [ "def quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000, quantizer=wavtokenizer):\n", " #audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", " audio = resample(audio_data, sampling_rate, 24000).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio=audio.squeeze(0)\n", " _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n", " codes = codes.squeeze(1).to(device)#+last_text_token\n", "\n", " return codes[0].tolist()#+last_text_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QgGSndp8AoVW" }, "outputs": [], "source": [ "def resample(audio: np.ndarray, sr: int, target_sr: int):\n", "\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(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "txxV2uboCYih" }, "outputs": [], "source": [ "def quantize_wavtokenizer(row, quantizer=wavtokenizer):\n", " audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", " audio = resample(audio_data, sample_rate, 24000).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " #print(audio.shape)\n", " #print(audio.dim())\n", " _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n", " codes = codes.squeeze(1).to(device)#+last_text_token\n", "\n", " return codes[0].tolist()#+last_text_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JDfRH6HUIGiX" }, "outputs": [], "source": [ "def decode_tokenizer(discrete_code):\n", " #discrete code is a list\n", " discrete_code=torch.tensor([discrete_code]).to(device)-last_text_token\n", " features = wavtokenizer.codes_to_features(discrete_code).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n", " return audio_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0U_45AQey40V" }, "outputs": [], "source": [ "def decode_tokenizer(discrete_code):\n", " #discrete code is a list\n", " discrete_code=torch.tensor([[discrete_code]]).to(device)#-last_text_token\n", " features = wavtokenizer.codes_to_features(discrete_code).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n", " return audio_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ij19rZw-fEQ0" }, "outputs": [], "source": [ "class PromptProcessor():\n", " def __init__(self,lang):\n", " self.lang=lang\n", " self.bos = \"<|im_start|>\"\n", " self.eos = \"<|im_end|>\"\n", " self.tts_prompt = \"{bos}\\n{tts}\\n{text_start}{words}{text_end}\\n{lang}\\n{audio_start}\\n\"\n", " self.stt_prompt = \"{bos}\\n{stt}\\n{audio_start}{codes}{audio_end}\\n{lang}\\n{text_start}\\n\"\n", " self.special_tokens = {\n", " \"audio_code\": \"<|{}|>\",\n", " \"tts\":\"<|tts|>\",\n", " \"stt\":\"<|stt|>\",\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", " }\n", " super().__init__()\n", "\n", "\n", " def create_results_prompts(self,words):\n", " prompt_audio= []\n", " prompt_text=[]\n", " all_tokens=[]\n", " for i in words:\n", " word = i[\"word\"]\n", " duration = self.special_tokens[\"time\"].format(i[\"duration\"])\n", " tokens = \"\".join([self.special_tokens[\"audio_code\"].format(c) for c in i[\"codes\"]])\n", " all_tokens.append(tokens)\n", " prompt_audio.append(f'{word}{duration}{self.special_tokens[\"code_start\"]}{tokens}{self.special_tokens[\"code_end\"]}')\n", " prompt_text.append(f'{tokens}{duration}{self.special_tokens[\"word_start\"]}{word}{self.special_tokens[\"word_end\"]}')\n", " return \"\".join(all_tokens),\"\\n\".join(prompt_audio),\"\\n\".join(prompt_text)\n", "\n", "\n", "\n", " def get_prompt(self, row):\n", " try:\n", " audio=torch.from_numpy(row[\"audio\"][\"array\"]).unsqueeze(0)#torch.tensor([row[\"audio\"][\"array\"]])\n", " #print(audio)\n", " sample_rate=row[\"audio\"][\"sampling_rate\"]\n", " if row[\"text\"]:\n", " transcript=row[\"text\"]\n", " else:\n", " transcript=row[\"transcript\"]\n", " input_words = ctc.process_text(transcript)\n", " words= ctc.align(audio,sample_rate,transcript)\n", " #print(words)\n", " inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n", " #self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{audio_start}\\n\"\n", " prompt_tts= self.tts_prompt.format(\n", " bos=self.bos,\n", " text_start=self.special_tokens['text_start'],\n", " tts=self.special_tokens['tts'],\n", " words=inputs_words_strings,\n", " lang=self.special_tokens[self.lang],\n", " text_end=self.special_tokens['text_end'],\n", " audio_start=self.special_tokens['audio_start']\n", " )\n", "\n", "\n", " all_codes, tts_extra, stt_extra=self.create_results_prompts(words)\n", " prompt_stt=self.stt_prompt.format(\n", " bos=self.bos,\n", " audio_start=self.special_tokens['audio_start'],\n", " stt=self.special_tokens['stt'],\n", " codes=all_codes,\n", " lang=self.special_tokens[self.lang],\n", "\n", " audio_end=self.special_tokens['audio_end'],\n", " text_start=self.special_tokens['text_start']\n", " )\n", " prompt_stt+=stt_extra+f\"\\n{self.special_tokens['text_end']}\\n{self.eos}\\n\"\n", " prompt_tts+=tts_extra+f\"\\n{self.special_tokens['audio_end']}\\n{self.eos}\\n\"\n", "\n", " return {\"stt\":prompt_stt,\"tts\":prompt_tts}\n", " except Exception as e:\n", " #print(e)\n", " return {\"stt\":\"An error occurred\",\"tts\":\"An error occurred\"}#,\"An error occured\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ctohbEGTfZYq" }, "outputs": [], "source": [ "ps=PromptProcessor(\"yoruba\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "9f80b9ce82aa4c2bb3e6da8edb4887ef", "4d77ee1fa6ed43efa05683b12cf26239" ] }, "id": "Q7R28b7gd-9f", "outputId": "0c44d8ba-582f-42ca-f859-acb9a52a5729" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f80b9ce82aa4c2bb3e6da8edb4887ef", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
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], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 53 } ], "source": [ "ps.get_prompt(k)[\"tts\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "43YFGwbEbWkN" }, "outputs": [], "source": [ "data_yoruba = data_yoruba.cast_column(\"audio\", Audio(sampling_rate=24000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6aDesKzcQZQn", "outputId": "455497c1-814c-40f8-a6b4-c9812880aa96" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['audio', 'text'],\n", " num_rows: 15188\n", "})" ] }, "metadata": {}, "execution_count": 56 } ], "source": [ "data_yoruba" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bMOmeJx5IkAn" }, "outputs": [], "source": [ "start=0\n", "end=len(data_yoruba)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "--KPdDtTvVrN", "outputId": "b0a99ed0-cfb7-416b-ed7f-7f52fa778517" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "15188\n" ] } ], "source": [ "print(end)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZtcDBjbQh39V" }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 806, "referenced_widgets": [ "e0b88a0e362c4a6a90007d6dbb7898f7", "d43a756456da4d90b0ff3a68f495b2a4", "10bf93a19adb4be98db0eef6a6d3e4b7", "61fb2ad3726249e7997db481f16ec38d", "583a4e6780ae4b5fb57ff7a9abcbb8c0", "bf5d385efe034480a6094d60cabb0494", "76f5c621fdb842e884114096a5f39e2b", "073b1c763bd745f6988bb9bd801327c0", "885207f1cd3441ad8957327a2a982ac6", "d7b3312c66d849598a7043e3e73b4737", "89d909976ce94c08a91a5efacbd3e62e", "46d7d1c3a76243619f326cf8c7b73fca", 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Uninstalling fsspec-2024.10.0:\n", " Successfully uninstalled fsspec-2024.10.0\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", "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\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" ] } ], "source": [ "!pip install datasets triton snac wandb accelerate torchdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m4uPM3IpnsEo", "outputId": "0e2fed8e-9448-426f-e5ad-c3403faf68bd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\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", "\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" ] } ], "source": [ "from outetts.wav_tokenizer.decoder import WavTokenizer\n", "from outetts.wav_tokenizer.encoder.utils import convert_audio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "543a-ZmC7xjE", "outputId": "3572c26c-f122-4fa3-aaa8-0cd93aeb13c1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EVyBedbQUM3F" }, "outputs": [], "source": [ "\n", "\n", "import os\n", "import torch\n", "import time\n", "import numpy as np\n", "import torchaudio\n", "#from snac import SNAC\n", "from tqdm import tqdm\n", "import huggingface_hub\n", "import shutil\n", "import soundfile as sf\n", "from torch.utils.data import DataLoader, Dataset\n", "from transformers import AdamW, get_linear_schedule_with_warmup, DataCollatorWithPadding\n", "from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets,Dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z8LFkziTgFRf" }, "outputs": [], "source": [ "import torchaudio\n", "import torch\n", "import torchaudio.functional as F\n", "import inflect\n", "import re\n", "import uroman as ur\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "c8b57e894816423d8001c6431f4fcb8f", "06360960a0c44095b4438561b56250c6" ] }, "id": "DN19SQCOUc6m", "outputId": "38921b10-5798-4cd0-ee64-746f9cbba82c" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c8b57e894816423d8001c6431f4fcb8f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
\",\n", " \"<|im_end|>\",\n", " \"<|text_start|>\",\n", " \"<|text_end|>\",\n", " \"<|audio_start|>\",\n", " \"<|audio_end|>\",\n", " \"<|code_start|>\",\n", " \"<|code_end|>\",\n", " \"<|text_sep|>\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2jHr-11YmoId", "outputId": "7c511577-3533-4201-a67b-70102ca53c3b" }, "outputs": [ { "data": { "text/plain": [ "2024" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "audio_tokens=[f\"<|{i}|>\" for i in range(0,2024)]\n", "tokenizer.add_tokens(audio_tokens)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xZKFh8sdmqWB", "outputId": "eec66d52-e0dd-4e33-8aef-8c2bcc91db74" }, "outputs": [ { "data": { "text/plain": [ "1000" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time_tokens=[f\"<|t_{round(i,2)}|>\" for i in np.arange(0,10,0.01)]#time_tokens\n", "tokenizer.add_tokens(time_tokens)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OJk2i5urKIec", "outputId": "779099fa-c0aa-4dee-8009-f11d953116b6" }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.eos_token_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6BPy5GpEKGP_" }, "outputs": [], "source": [ "tokenizer.pad_token_id=0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "N90blgKsHJo6", "outputId": "07cbd6bb-4209-4751-875b-2214f688c62d" }, "outputs": [ { "data": { "text/plain": [ "52183" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(tokenizer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AyP1GBHDoO4v" }, "outputs": [], "source": [ "total_tokens=49152+4096" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tMpZOv8gVya2", "outputId": "3b26d2dc-051a-411b-dfd6-96a45fd5bb5f" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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" ] }, { "data": { "text/plain": [ "Embedding(53248, 960)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.resize_token_embeddings(total_tokens)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1yjGTRLMWI26" }, "outputs": [], "source": [ "model=torch.compile(model.to(device))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QOxVQVeZWL1d", "outputId": "775ef716-27b0-4e4b-a0a9-b9547eacd55c" }, "outputs": [ { "data": { "text/plain": [ "731.510784" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.get_memory_footprint()/ 1e6" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Zqe1ZczmWP1b", "outputId": "f7c64a79-d17a-4694-bc8a-502f9bbbb6f7" }, "outputs": [ { "data": { "text/plain": [ "365753280" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.num_parameters()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IYyt-dhuWx9q", "outputId": "08a27722-2365-44b2-ec22-62914a7b8f1e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/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", " WeightNorm.apply(module, name, dim)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "making attention of type 'vanilla' with 768 in_channels\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/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", " state_dict_raw = torch.load(model_path, map_location=\"cpu\")['state_dict']\n" ] } ], "source": [ "config_path = \"/content/drive/MyDrive/audio_datasets/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\"\n", "model_path = \"/content/drive/MyDrive/audio_datasets/wavtokenizer_large_speech_320_24k.ckpt\"#\"/content/wavtokenizer_medium_speech_320_24k_v2.ckpt\"\n", "wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)\n", "wavtokenizer = wavtokenizer.to(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "275g7SweCKAe" }, "outputs": [], "source": [ "def resample(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(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "N85dYwCmWZG8" }, "outputs": [], "source": [ "def quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000, quantizer=wavtokenizer):\n", " #audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", " audio = resample(audio_data, sampling_rate, 24000).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio=audio.squeeze(0)\n", " _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n", " codes = codes.squeeze(1).to(device)#+last_text_token\n", "\n", " return codes[0].tolist()#+last_text_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "txxV2uboCYih" }, "outputs": [], "source": [ "def quantize_wavtokenizer(row, quantizer=wavtokenizer):\n", " audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", " audio = resample(audio_data, sample_rate, 24000).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " #print(audio.shape)\n", " #print(audio.dim())\n", " _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n", " codes = codes.squeeze(1).to(device)#+last_text_token\n", "\n", " return codes[0].tolist()#+last_text_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0U_45AQey40V" }, "outputs": [], "source": [ "def decode_tokenizer(discrete_code):\n", " #discrete code is a list\n", " discrete_code=torch.tensor([[discrete_code]]).to(device)#-last_text_token\n", " features = wavtokenizer.codes_to_features(discrete_code).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n", " return audio_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gRdr07gcLN7H" }, "outputs": [], "source": [ "train_data=pd.read_csv(\"/content/drive/MyDrive/Tokenized2/all_data.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 461 }, "id": "t9rddLyYLgnh", "outputId": "a73f13da-9bd7-48fe-8039-c06355a8d983" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "train_data" }, "text/html": [ "\n", "
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"https://localhost:8080/" }, "id": "1oYlD-iTIFXw", "outputId": "93a3d98b-5ba4-44f2-8069-ae127a2bc08c" }, "outputs": [ { "data": { "text/plain": [ "(642150, 3)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mR8OImavy1Z5", "outputId": "4664f2a2-c722-43e4-c7d3-e6fff69513fd" }, "outputs": [ { "data": { "text/plain": [ "3999" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data[\"length\"].max()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AFTfQ4RHIyw8", "outputId": "3fae2b3f-1920-4230-ecef-813e82382b30" }, "outputs": [ { "data": { "text/plain": [ "(642150, 3)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nDWFPH4YI4q-" }, "outputs": [], "source": [ "#train_data=train_data.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "t0QWUWIlI-U9" }, "outputs": [], "source": [ "\n", "from datasets import Dataset\n", "train_dataset=Dataset.from_pandas(train_data[[\"0\"]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QepGS-a8qSJL" }, "outputs": [], "source": [ "train_dataset=train_dataset.shuffle()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Eoxv09xaGsM_" }, "outputs": [], "source": [ "from torch.utils.data import DataLoader, Dataset\n", "class YarnDataset(Dataset):\n", " def __init__(self,dataset):\n", " self.ds = dataset\n", " super().__init__()\n", "\n", " def __len__(self):\n", " return len(self.ds)\n", "\n", "\n", " def __getitem__(self, idx):\n", " prompt=self.ds[idx][\"0\"]\n", " #print(prompt)\n", " return tokenizer(prompt,)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PdiQt7_Ctlbb" }, "outputs": [], "source": [ "yarn_dataset = YarnDataset(train_dataset)#train_dataset.select(rang(0600000))#CustomDataSetAfriSpeech(data)#" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QJBKk_oXQ3HP" }, "outputs": [], "source": [ "batch_size=4\n", "learning_rate=1e-3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qPkuxPZIO1dP" }, "outputs": [], "source": [ "# Initialize data collator\n", "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n", "\n", "# Create DataLoader with collate_fn using data collator\n", "dataloader = DataLoader(\n", " yarn_dataset,\n", " batch_size=batch_size,\n", " collate_fn=data_collator,shuffle=True # Automatically handles padding\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XHZZVQBQRaXi", "outputId": "ab1af510-f5f9-4944-e6b7-8e23da2c907a" }, "outputs": [ { "data": { "text/plain": [ "642150" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(yarn_dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mpqTRQC50jEy", "outputId": "653029cd-43b6-4aae-fc2d-d55448d1a8cb" }, "outputs": [ { "data": { "text/plain": [ "{'input_ids': [1, 198, 49152, 257, 3296, 363, 49158, 48187, 49158, 89, 49158, 17076, 49158, 13671, 49158, 15727, 264, 49158, 349, 49158, 12609, 49158, 89, 49158, 12730, 49158, 81, 49158, 322, 524, 49158, 38984, 49158, 8320, 49158, 522, 49158, 1595, 447, 274, 49158, 6859, 541, 49158, 84, 1866, 49158, 2016, 49158, 1558, 49158, 4350, 49158, 10128, 49158, 1531, 49158, 6859, 541, 49158, 373, 1457, 49158, 1766, 49158, 5588, 49158, 306, 49158, 99, 21127, 49158, 2334, 49158, 84, 517, 49158, 404, 49158, 39139, 49158, 11527, 49158, 1710, 49158, 11355, 49158, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yarn_dataset[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "87LfDjTFyj6y", "outputId": "20632a96-8642-49e2-b873-e0e9d2dd101c" }, "outputs": [ { "data": { "text/plain": [ "160538" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataloader)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1Ez7HQd7GFEA" }, "outputs": [], "source": [ "from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts\n", "from torch.optim.lr_scheduler import LambdaLR\n", "from transformers import get_linear_schedule_with_warmup,get_cosine_schedule_with_warmup,get_constant_schedule_with_warmup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kBWFROqbzO3h" }, "outputs": [], "source": [ "def get_lr_lambda(step):\n", " if step < lr_warmup_steps:\n", " # Linear warmup\n", " return step / lr_warmup_steps\n", " elif step >=(num_decay_start):\n", " return 1-(step-num_decay_start)/(num_training_steps-num_decay_start)\n", " else:\n", " # Constant learning rate\n", " return 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "18ObtWm1Ryr3" }, "outputs": [], "source": [ "#0.2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AVI3iLgUbYnW", "outputId": "432aef8c-f0c8-4bbd-bd43-2bbaf235e5df" }, "outputs": [ { "data": { "text/plain": [ "OptimizedModule(\n", " (_orig_mod): LlamaForCausalLM(\n", " (model): LlamaModel(\n", " (embed_tokens): Embedding(53248, 960)\n", " (layers): ModuleList(\n", " (0-31): 32 x LlamaDecoderLayer(\n", " (self_attn): LlamaSdpaAttention(\n", " (q_proj): Linear(in_features=960, out_features=960, bias=False)\n", " (k_proj): Linear(in_features=960, out_features=320, bias=False)\n", " (v_proj): Linear(in_features=960, out_features=320, bias=False)\n", " (o_proj): Linear(in_features=960, out_features=960, bias=False)\n", " (rotary_emb): LlamaRotaryEmbedding()\n", " )\n", " (mlp): LlamaMLP(\n", " (gate_proj): Linear(in_features=960, out_features=2560, bias=False)\n", " (up_proj): Linear(in_features=960, out_features=2560, bias=False)\n", " (down_proj): Linear(in_features=2560, out_features=960, bias=False)\n", " (act_fn): SiLU()\n", " )\n", " (input_layernorm): LlamaRMSNorm((960,), eps=1e-05)\n", " (post_attention_layernorm): LlamaRMSNorm((960,), eps=1e-05)\n", " )\n", " )\n", " (norm): LlamaRMSNorm((960,), eps=1e-05)\n", " (rotary_emb): LlamaRotaryEmbedding()\n", " )\n", " (lm_head): Linear(in_features=960, out_features=53248, bias=False)\n", " )\n", ")" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_epochs=5\n", "optimizer = AdamW(model.parameters(), lr=learning_rate, betas=(0.9, 0.95),weight_decay=0.01)\n", "lr_warmup_steps=50\n", "\n", "num_training_steps=1255*num_epochs\n", "num_decay_start=50#num_training_steps#-20\n", "#scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=T_0, T_mult=T_mult, eta_min=eta_min)\n", "#scheduler = # Create LambdaLR scheduler\n", "scheduler = LambdaLR(optimizer, lr_lambda=get_lr_lambda) #get_constant_schedule_with_warmup(optimizer,num_warmup_steps=10)#\n", "global_step = 0\n", "accumulation_steps = int(512/batch_size)#32\n", "model.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "j9vN0iRPYbRa" }, "outputs": [], "source": [ "new_checkpoint=\"saheedniyi/YarnGPT\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "9aae5f48357c4b928b1926d3df5a2692", "c0417e03d3a742678bfcefa694fdc759" ] }, "id": "V0k8jG6Q2iMP", "outputId": "4e64adf2-43b4-43f9-9212-0cff5d76956b" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9aae5f48357c4b928b1926d3df5a2692", "version_major": 2, "version_minor": 0 }, "text/plain": [ "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", " checkpoint=torch.load(\"/content/drive/MyDrive/model_final/final_0lastxepoch.pt\")\n" ] } ], "source": [ "\n", "#checkpoint=torch.load(\"/content/drive/MyDrive/model_final/final_0lastxepoch.pt\")\n", "#optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n", "#model.load_state_dict(checkpoint['model_state_dict'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eP0TyvltVUpO", "outputId": "78bc548a-c830-4a46-cafe-71c85eb81489" }, "outputs": [ { "data": { "text/plain": [ "device(type='cuda')" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "device" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JVpdMTfeKJzL", "outputId": "89b446bb-7398-4639-85d5-1fcdc3bdb8e3" }, "outputs": [ { "data": { "text/plain": [ "160538" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataloader)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/", "referenced_widgets": [ "76eba6ef2bda4a1fa99212db55e34114", "e4bd57b66fe848989f5d240302bbd05f" ] }, "id": "G1qPorNycNvM", "outputId": "6344383b-837e-4ff2-faf9-5970ec104fc9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'loss': '2.8443892002105713', 'num_iter': 512, 'lr': 1.25e-06, 'time': '28.37386131286621 Seconds', 'norm': 0.11083984375}\n", "{'loss': '2.8334364891052246', 'num_iter': 1024, 'lr': 2.5e-06, 'time': '29.3117778301239 Seconds', 'norm': 0.07275390625}\n", "{'loss': '2.849212169647217', 'num_iter': 1536, 'lr': 3.75e-06, 'time': '29.817814588546753 Seconds', 'norm': 0.0712890625}\n", "{'loss': '2.8385062217712402', 'num_iter': 2048, 'lr': 5e-06, 'time': '29.98392081260681 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#model.push_to_hub(new_checkpoint,private=False,commit_message=f\"model {epoch} {(i+1)*batch_size}\")\n", " model.train()\n", " optimizer.step()\n", " torch.save({\n", " 'epoch': epoch,\n", " 'model_state_dict': model.state_dict(),\n", " 'optimizer_state_dict': optimizer.state_dict(),\n", " 'scheduler_state_dict':scheduler.state_dict(),\n", " 'loss': loss,\n", " 'global_step':global_step\n", " },f'/content/drive/MyDrive/model_final/final_{epoch}lastfitepoch.pt')\n", "model.push_to_hub(new_checkpoint,private=False,commit_message=f\"final\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 101, "referenced_widgets": [ "f4cea139f7ca41ffaea7d0f884a37e95", "3d1d8ac6d9094a2b836ab125b278a457", "081143281af740c297df324c5011f330", "ea1eb880c4034694979b614eb62794a6", "cd906cadab1d4bb89c8446c03993c62d", "cd26e2543d7243809eda41d59d85e965", "e4a747a713ca4a519c3418c22a79f29f", "a959621403cb4080a3d2ade774985b3f", "dca48d9964514e19a684b9d25f368aa9", "0ddbbf857bbe459d95d43db14ab9f6a6", "041e24b246224764b03450eae1447c50" ] }, "id": "6GwywbGrlMKb", "outputId": "e5bc661b-acff-4a21-d748-b132c0c4581f" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f4cea139f7ca41ffaea7d0f884a37e95", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors: 0%| | 0.00/732M [00:00=4.46.1 in /usr/local/lib/python3.10/dist-packages (from outetts) (4.47.1)\n", "Collecting pytorch-lightning (from outetts)\n", " Downloading pytorch_lightning-2.5.0.post0-py3-none-any.whl.metadata (21 kB)\n", "Collecting tensorboardX (from outetts)\n", " Downloading tensorboardX-2.6.2.2-py2.py3-none-any.whl.metadata (5.8 kB)\n", "Requirement already satisfied: soundfile in /usr/local/lib/python3.10/dist-packages (from outetts) (0.13.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from outetts) (1.26.4)\n", "Collecting jsonargparse (from 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collected packages: openai-whisper, encodec, pesq, unidic-lite\n", " Building wheel for openai-whisper (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for openai-whisper: filename=openai_whisper-20240930-py3-none-any.whl size=803373 sha256=dd63697d5f2380f1444bc5fe1dc31a3f87a270ec30cb9b937637ec567e330f74\n", " Stored in directory: /root/.cache/pip/wheels/dd/4a/1f/d1c4bf3b9133c8168fe617ed979cab7b14fe381d059ffb9d83\n", " Building wheel for encodec (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for encodec: filename=encodec-0.1.1-py3-none-any.whl size=45760 sha256=adf14d590c9e74786104dff1762303f43bc6c752e3f1236446ae6305cb515579\n", " Stored in directory: /root/.cache/pip/wheels/fc/36/cb/81af8b985a5f5e0815312d5e52b41263237af07b977e6bcbf3\n", " Building wheel for pesq (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for pesq: filename=pesq-0.0.4-cp310-cp310-linux_x86_64.whl size=262943 sha256=f0d6f54d68b8b9288a2fc6c590cd6bdbeb81726e2e20d5f2b8c0247d4a6f070b\n", " Stored in directory: /root/.cache/pip/wheels/c5/4e/2c/251524370c0fdd659e99639a0fbd0ca5a782c3aafcd456b28d\n", " Building wheel for unidic-lite (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for unidic-lite: filename=unidic_lite-1.0.8-py3-none-any.whl size=47658818 sha256=e89007bef2e730232d99961e7a6c9bca61d80047f53366475e01b6c78a72aae1\n", " Stored in directory: /root/.cache/pip/wheels/89/e8/68/f9ac36b8cc6c8b3c96888cd57434abed96595d444f42243853\n", "Successfully built openai-whisper encodec pesq unidic-lite\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", "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" ] } ], "source": [ "pip install outetts uroman" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HgJjekSOT8iX", "outputId": "2f0873ae-60ff-49a3-eb76-f82c36fe2390" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting datasets\n", " Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n", "Requirement already satisfied: triton in /usr/local/lib/python3.10/dist-packages (3.1.0)\n", "Collecting snac\n", " Downloading snac-1.2.1-py3-none-any.whl.metadata (3.5 kB)\n", "Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.19.1)\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages 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"\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", "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\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", "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, torchdata, snac, datasets\n", " Attempting uninstall: fsspec\n", " Found existing installation: fsspec 2024.10.0\n", " Uninstalling fsspec-2024.10.0:\n", " Successfully uninstalled fsspec-2024.10.0\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", "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\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" ] } ], "source": [ "!pip install datasets triton snac wandb accelerate torchdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m4uPM3IpnsEo", "outputId": "63a19431-04b3-49d3-da29-1119152ed72e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\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", "\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" ] } ], "source": [ "from outetts.wav_tokenizer.decoder import WavTokenizer\n", "from outetts.wav_tokenizer.encoder.utils import convert_audio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "543a-ZmC7xjE", "outputId": "7b8f7b74-991f-4680-c930-39511544f3af" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EVyBedbQUM3F" }, "outputs": [], "source": [ "\n", "\n", "import os\n", "import torch\n", "import time\n", "import numpy as np\n", "import torchaudio\n", "#from snac import SNAC\n", "from tqdm import tqdm\n", "import huggingface_hub\n", "import shutil\n", "import soundfile as sf\n", "from torch.utils.data import DataLoader, Dataset\n", "from transformers import AdamW, get_linear_schedule_with_warmup, DataCollatorWithPadding\n", "from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets,Dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z8LFkziTgFRf" }, "outputs": [], "source": [ "import torchaudio\n", "import torch\n", "import torchaudio.functional as F\n", "import inflect\n", "import re\n", "import uroman as ur\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "70f2b1a35f414c798a8300c74e1d1be0", "02848bb5b7494a4fa7fa9a05aa4ac2bc" ] }, "id": "DN19SQCOUc6m", "outputId": "f602c414-4208-4605-d57b-a0d4a7ce0fff" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "70f2b1a35f414c798a8300c74e1d1be0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
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"https://localhost:8080/" }, "id": "1oYlD-iTIFXw", "outputId": "f0d3b529-d6ab-4570-8ea2-7c3f34b25081" }, "outputs": [ { "data": { "text/plain": [ "(1929276, 3)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mR8OImavy1Z5", "outputId": "1482fcfc-41f6-4529-af5c-aa848821ebda" }, "outputs": [ { "data": { "text/plain": [ "3462" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data[\"length\"].max()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AFTfQ4RHIyw8", "outputId": "01e8262a-4981-4cfd-da18-c4f72e99afa6" }, "outputs": [ { "data": { "text/plain": [ "(1929276, 3)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nDWFPH4YI4q-" }, "outputs": [], "source": [ "#train_data=train_data.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "t0QWUWIlI-U9" }, "outputs": [], "source": [ "\n", "from datasets import Dataset\n", "train_dataset=Dataset.from_pandas(train_data[[\"tts\"]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QepGS-a8qSJL" }, "outputs": [], "source": [ "train_dataset=train_dataset.shuffle()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ToMeB3qx6i_V" }, "outputs": [], "source": [ "train_dataset=train_dataset.shuffle()" ] }, { "cell_type": "code", "source": [ "train_dataset=train_dataset.shuffle()" ], "metadata": { "id": "t_pjjZL194-5" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_dataset=train_dataset.shuffle()" ], "metadata": { "id": "mDN0r319953f" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Eoxv09xaGsM_" }, "outputs": [], "source": [ "from torch.utils.data import DataLoader, Dataset\n", "class YarnDataset(Dataset):\n", " def __init__(self,dataset):\n", " self.ds = dataset\n", " super().__init__()\n", "\n", " def __len__(self):\n", " return len(self.ds)\n", "\n", "\n", " def __getitem__(self, idx):\n", " prompt=self.ds[idx][\"tts\"]\n", " #print(prompt)\n", " return tokenizer(prompt,)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PdiQt7_Ctlbb" }, "outputs": [], "source": [ "yarn_dataset = YarnDataset(train_dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QJBKk_oXQ3HP" }, "outputs": [], "source": [ "batch_size=4\n", "learning_rate=1e-3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qPkuxPZIO1dP" }, "outputs": [], "source": [ "# Initialize data collator\n", "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n", "\n", "# Create DataLoader with collate_fn using data collator\n", "dataloader = DataLoader(\n", " yarn_dataset,\n", " batch_size=batch_size,\n", " collate_fn=data_collator,shuffle=True # Automatically handles padding\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XHZZVQBQRaXi", "outputId": "03404215-aa7d-4672-b24b-b8341a747c51" }, "outputs": [ { "data": { "text/plain": [ "1929276" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(yarn_dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mpqTRQC50jEy", "outputId": "e99d7e6f-3334-45b0-f658-6e6a960a4798" }, "outputs": [ { "data": { "text/plain": [ "{'input_ids': [1, 198, 49152, 14765, 49158, 721, 518, 49158, 10374, 49158, 270, 101, 49158, 93, 49158, 5568, 518, 49158, 3250, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yarn_dataset[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "87LfDjTFyj6y", "outputId": "f98d4b12-13cb-4ae1-898b-a9080a084e28" }, "outputs": [ { "data": { "text/plain": [ "482319" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataloader)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_NJ4gP3_2Cp-", "outputId": "a3ddfd13-1e34-4686-eb8a-cdcd4655bac7" }, "outputs": [ { "data": { "text/plain": [ "942.029296875" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "482319/512" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dUtacXKt2Iw1", "outputId": "d800f7b0-6785-4e46-8ddd-522acf58b7ef" }, "outputs": [ { "data": { "text/plain": [ "1250.0" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "640000/512" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M340iQMK2PiF", "outputId": "f61ad845-1472-4cbe-b02a-63e0271aa6d8" }, "outputs": [ { "data": { "text/plain": [ "942.029296875" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1929276/(512*4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xfpWBncA2dK9", "outputId": "27fe6dd5-34ff-40bc-92fc-e3540ffe3857" }, "outputs": [ { "data": { "text/plain": [ "942.029296875" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "482319/512" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1Ez7HQd7GFEA" }, "outputs": [], "source": [ "from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts\n", "from torch.optim.lr_scheduler import LambdaLR\n", "from transformers import get_linear_schedule_with_warmup,get_cosine_schedule_with_warmup,get_constant_schedule_with_warmup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kBWFROqbzO3h" }, "outputs": [], "source": [ "def get_lr_lambda(step):\n", " if step < lr_warmup_steps:\n", " # Linear warmup\n", " return step / lr_warmup_steps\n", " elif step >=(num_decay_start):\n", " return 1-(step-num_decay_start)/(num_training_steps-num_decay_start)\n", " else:\n", " # Constant learning rate\n", " return 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "18ObtWm1Ryr3" }, "outputs": [], "source": [ "#0.2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AVI3iLgUbYnW", "outputId": "9e2b09ad-b2e6-4699-b834-c75674b927bc" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/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", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "OptimizedModule(\n", " (_orig_mod): LlamaForCausalLM(\n", " (model): LlamaModel(\n", " (embed_tokens): Embedding(53248, 960)\n", " (layers): ModuleList(\n", " (0-31): 32 x LlamaDecoderLayer(\n", " (self_attn): LlamaSdpaAttention(\n", " (q_proj): Linear(in_features=960, out_features=960, bias=False)\n", " (k_proj): Linear(in_features=960, out_features=320, bias=False)\n", " (v_proj): Linear(in_features=960, out_features=320, bias=False)\n", " (o_proj): Linear(in_features=960, out_features=960, bias=False)\n", " (rotary_emb): LlamaRotaryEmbedding()\n", " )\n", " (mlp): LlamaMLP(\n", " (gate_proj): Linear(in_features=960, out_features=2560, bias=False)\n", " (up_proj): Linear(in_features=960, out_features=2560, bias=False)\n", " (down_proj): Linear(in_features=2560, out_features=960, bias=False)\n", " (act_fn): SiLU()\n", " )\n", " (input_layernorm): LlamaRMSNorm((960,), eps=1e-05)\n", " (post_attention_layernorm): LlamaRMSNorm((960,), eps=1e-05)\n", " )\n", " )\n", " (norm): LlamaRMSNorm((960,), eps=1e-05)\n", " (rotary_emb): LlamaRotaryEmbedding()\n", " )\n", " (lm_head): Linear(in_features=960, out_features=53248, bias=False)\n", " )\n", ")" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_epochs=2\n", "optimizer = AdamW(model.parameters(), lr=learning_rate, betas=(0.9, 0.95),weight_decay=0.01)\n", "lr_warmup_steps=200\n", "\n", "num_training_steps=1255*num_epochs\n", "num_decay_start=50#num_training_steps#-20\n", "#scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=T_0, T_mult=T_mult, eta_min=eta_min)\n", "#scheduler = # Create LambdaLR scheduler\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", "global_step = 0\n", "accumulation_steps = int(512/batch_size)#32\n", "model.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "j9vN0iRPYbRa" }, "outputs": [], "source": [ "new_checkpoint=\"saheedniyi/YarnGPT-local\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "3d687f99d4564f4e9efc1f988f5d6799", "5625d8053fb64e00a587464d8800a25c" ] }, "id": "V0k8jG6Q2iMP", "outputId": "c8665e1c-e876-48de-cd97-82a25086ff0e" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3d687f99d4564f4e9efc1f988f5d6799", "version_major": 2, "version_minor": 0 }, "text/plain": [ "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", " checkpoint=torch.load(\"/content/drive/MyDrive/YarnGPT_naij/final_{epoch}epoch.pt\")\n" ] }, { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '/content/drive/MyDrive/YarnGPT_naij/final_{epoch}epoch.pt'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\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", "\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", "\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 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\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 \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[0m\u001b[1;32m 641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/YarnGPT_naij/final_{epoch}epoch.pt'" ] } ], "source": [ "\n", "checkpoint=torch.load(\"/content/drive/MyDrive/YarnGPT_naij/final_1epoch.pt\")\n", "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n", "#model.load_state_dict(checkpoint['model_state_dict'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eP0TyvltVUpO" }, "outputs": [], "source": [ "device" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JVpdMTfeKJzL", "outputId": "53e282a6-80cf-4ee7-a40b-ac94d02a9399" }, "outputs": [ { "data": { "text/plain": [ "482319" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataloader)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/" }, "id": "G1qPorNycNvM", "outputId": "7d92557d-2086-4769-aa16-db4f40f3db4b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'loss': '2.3553764820098877', 'num_iter': 512, 'lr': 5e-06, 'time': '518.3966403007507 Seconds', 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optimizer.zero_grad(set_to_none=True)\n", " scheduler.step()\n", " global_step += 1\n", "\n", "\n", " # Get and format the learning rate\n", " #lr_rate = scheduler.get_last_lr()[0]\n", " t1=time.time()\n", " dt=t1-t0\n", " logs={\"loss\": f\"{loss_accum}\", \"step\": global_step,\"num_iter\":batch_size*(i+1),\"lr\":scheduler.get_last_lr()[0],\"time\":f\"{dt} Seconds\",\"norm\":norm.item()}\n", " print(logs)\n", " with open(\"/content/drive/MyDrive/YarnGPT_naij/logs.json\", \"a\") as file:\n", " json.dump(logs, file)\n", " t0=time.time()\n", " loss_accum=0\n", " if (i>0) and (i + 1) % (2*8192)== 0:\n", " torch.save({\n", " 'epoch': epoch,\n", " 'model_state_dict': model.state_dict(),\n", " 'optimizer_state_dict': optimizer.state_dict(),\n", " 'scheduler_state_dict':scheduler.state_dict(),\n", " 'loss': loss,\n", " 'global_step':global_step\n", " },f'/content/drive/MyDrive/YarnGPT_naij/{i*batch_size}_{epoch}xtraepoch.pt')\n", "\n", "\n", " 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"_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4661d7cb90414655b7ea77f615bb99cb", "placeholder": "​", "style": "IPY_MODEL_07824afeabf1432da02e4eee4a2d26e3", "value": " 4.08M/4.08M [00:01<00:00, 3.82MB/s]" } } } } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: python-wrapper/README.md ================================================ # YarnGPT Python Wrapper Library ## Description YarnGPT 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. ## Features - Supports 6 preset voices (idera, jude, joke, umar, osagie, onye) - Utilizes Hugging Face's model caching for efficient model loading - Exposes a straightforward API function: generate_speech(text, speaker, temperature, repetition_penalty, max_length) - Allows customization of generation parameters such as temperature, repetition penalty, and maximum token length - Includes unit tests to ensure core functionality ## Installation 1. Create and activate a virtual environment: - On Linux/MacOS: ```bash python3 -m venv env source env/bin/activate ``` - On Windows: ```bash python -m venv env env\Scripts\activate ``` 2. Install the package: ```bash pip install yarngpt ``` ## Usage Basic usage to generate and save audio: ```python from yarngpt import generate_speech import torchaudio # Generate speech with the default speaker (idera) audio = generate_speech("Hello, this is a test.") # Save the generated audio torchaudio.save("output.wav", audio, sample_rate=24000) ``` For Jupyter Notebook users, you can also play the audio directly: ```python from yarngpt import generate_speech import torchaudio from IPython.display import Audio # Generate and save speech audio = generate_speech("Hello, this is a test.", speaker="joke") torchaudio.save("output.wav", audio, sample_rate=24000) # Play the audio in the notebook Audio("output.wav") ``` ## Parameter Options - `text`: The input string to convert to speech - `speaker`: Choose from available speakers: idera, jude, joke, umar, osagie, onye (default is "idera") - `temperature`: Controls the randomness of generation (default is 0.1) - `repetition_penalty`: A factor to reduce repetitive output (default is 1.1) - `max_length`: The maximum length of the generated output tokens (default is 4000) ## Testing Run the unit tests to verify functionality: ```bash python -m unittest discover -s tests ``` ## License This project is licensed under the MIT License. ## Acknowledgments - Built as a contribution to yarngpt projects - Utilizes Hugging Face's model caching and the transformers library - Special thanks to the open-source community for their ongoing support For more details and documentation, visit the GitHub repository: https://github.com/jerryola1 ================================================ FILE: python-wrapper/audiotokenizer.py ================================================ import os import re import json import torch import inflect import random import uroman as ur import numpy as np import torchaudio from transformers import AutoTokenizer from outetts.wav_tokenizer.decoder import WavTokenizer from outetts.wav_tokenizer.encoder.utils import convert_audio class AudioTokenizer: def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n" self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) self.bos = "<|im_start|>" self.eos = "<|im_end|>" self.input_length=0 self.special_tokens = { "audio_code": "<|{}|>", "text_start": "<|text_start|>", "text_end": "<|text_end|>", "audio_start": "<|audio_start|>", "audio_end": "<|audio_end|>", "time": "<|t_{:.2f}|>", "code_start": "<|code_start|>", "code_end": "<|code_end|>", "text_sep": "<|text_sep|>" } self.lec = inflect.engine() #self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n" #self.config_path = "/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml" #self.model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt" self.wavtokenizer = WavTokenizer.from_pretrained0802(wav_tokenizer_config_path, wav_tokenizer_model_path) self.wavtokenizer = self.wavtokenizer.to(self.device) self.BASE_DIR = os.path.dirname(__file__) self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers") self.speakers=["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye"] def get_speaker_path(self,speaker_name): return os.path.join(self.DEFAULT_SPEAKERS_DIR, f"{speaker_name}.json") def load_speaker(self, path: str): with open(path, "r") as f: return json.load(f) def load_default_speaker(self, name: str): name = name.lower().strip() speaker_path=self.get_speaker_path(name) return self.load_speaker(speaker_path) def process_text(self, text: str): text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower()) text = re.sub(r'[-_/,\.\\]', ' ', text) text = re.sub(r'[^a-z\s]', '', text) text = re.sub(r'\s+', ' ', text).strip() return text.split() def create_audio_prompt(self,words: list) -> str: prompt = [] for i in words: word = i["word"] duration = self.special_tokens["time"].format(float(i["duration"])) tokens = "".join([self.special_tokens["audio_code"].format(c) for c in i["codes"]]) prompt.append(f'{word}{duration}{self.special_tokens["code_start"]}{tokens}{self.special_tokens["code_end"]}') return "\n".join(prompt) def create_prompt(self,text,speaker_name="idera"): speaker=self.load_default_speaker(speaker_name) input_words = self.process_text(speaker["text"]) + self.process_text(text) #input_words = process_text(speaker["text"]) + input_words inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words]) prompt = self.text_prompt.format( bos=self.bos, text_start=self.special_tokens['text_start'], words=inputs_words_strings, text_end=self.special_tokens['text_end'], audio_start=self.special_tokens['audio_start'] ) prompt += self.create_audio_prompt(speaker["words"]) return prompt def tokenize_prompt(self, prompt): input_ids = self.tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ).to(self.device) self.input_length=input_ids.shape[1] return input_ids.to(self.device) def get_audio(self,discrete_code): discrete_code=torch.tensor([[discrete_code]]).to(self.device) features = self.wavtokenizer.codes_to_features(discrete_code).to(self.device) bandwidth_id = torch.tensor([0]).to(self.device) audio_out = self.wavtokenizer.decode(features, bandwidth_id=bandwidth_id) return audio_out.to("cpu") def extract_integers(self,s): # Match integers enclosed in vertical bars |integer| matches = re.findall(r'\|(-?\d+)\|', s) # Convert matches to integers return [int(match) for match in matches] def get_codes(self, output): new_output=self.tokenizer.decode(output[0][self.input_length:]) codes=self.extract_integers(new_output) return codes class AudioTokenizerForLocal(AudioTokenizer): def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,): super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path) self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n" self.special_tokens = { "audio_code": "<|{}|>", "text_start": "<|text_start|>", "text_end": "<|text_end|>", "audio_start": "<|audio_start|>", "audio_end": "<|audio_end|>", "word_start": "<|word_start|>", "word_end": "<|word_end|>", "time": "<|t_{:.2f}|>", "code_start": "<|code_start|>", "code_end": "<|code_end|>", "text_sep": "<|text_sep|>", "hausa":"<|hausa|>", "igbo":"<|igbo|>", "yoruba":"<|yoruba|>", } self.uroman = ur.Uroman() self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers_local") self.speakers = [ "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2", "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2" ] def process_text(self, text: str): text = self.uroman.romanize_string(text) text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower()) text = re.sub(r'[-_/,\.\\]', ' ', text) text = re.sub(r'[^a-z\s]', '', text) text = re.sub(r'\s+', ' ', text).strip() return text.split() def create_prompt(self,text,lang,speaker_name=None): assert lang in ["hausa","igbo","yoruba"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba']" #if no speaker if speaker_name is None: if lang=="hausa": speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"]) elif lang=="igbo": speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"]) else: speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"]) speaker=self.load_default_speaker(speaker_name) input_words = self.process_text(speaker["text"]) + self.process_text(text) #input_words = process_text(speaker["text"]) + input_words inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words]) prompt = self.text_prompt.format( bos=self.bos, text_start=self.special_tokens['text_start'], words=inputs_words_strings, text_end=self.special_tokens['text_end'], lang=self.special_tokens[lang], audio_start=self.special_tokens['audio_start'] ) prompt += self.create_audio_prompt(speaker["words"]) return prompt class AudioTokenizerV2(AudioTokenizer): def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,): super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path) self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n" self.asr_prompt="{bos}\n{code_start}{codes}{code_end}\n{asr}\n" self.special_tokens = { "audio_code": "<|{}|>", "text_start": "<|text_start|>", "text_end": "<|text_end|>", "audio_start": "<|audio_start|>", "audio_end": "<|audio_end|>", "word_start": "<|word_start|>", "word_end": "<|word_end|>", "time": "<|t_{:.2f}|>", "code_start": "<|code_start|>", "code_end": "<|code_end|>", "text_sep": "<|text_sep|>", "hausa":"<|hausa|>", "igbo":"<|igbo|>", "yoruba":"<|yoruba|>", "english":"<|english|>",#<|english|> "asr":"<|asr|>" } self.uroman = ur.Uroman() self.DEFAULT_SPEAKERS_DIR_LOCAL = os.path.join(self.BASE_DIR, "default_speakers_local") self.DEFAULT_SPEAKERS_ENG = os.path.join(self.BASE_DIR, "default_speakers") self.speakers_local = [ "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2", "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2" ] self.speakers_eng = ["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye","saheed"] self.changed_tokens=[('<|1836|>', '<|453|><|453|>'), ('<|1837|>', '<|1836|><|1836|>'), ('<|1838|>', '<|1837|><|1837|>'), ('<|1840|>', '<|244|><|167|>'), ('<|1841|>', '<|235|><|219|>'), ('<|1844|>', '<|453|><|244|>'), ('<|1845|>', '<|1838|><|1838|>')] def process_text(self, text: str): text = self.uroman.romanize_string(text) text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower()) text = re.sub(r'[-_/,\.\\]', ' ', text) text = re.sub(r'[^a-z\s]', '', text) text = re.sub(r'\s+', ' ', text).strip() return text.split() def get_speaker_path(self,speaker_name,dir): return os.path.join(dir, f"{speaker_name}.json") def load_speaker(self, path: str): with open(path, "r") as f: return json.load(f) def load_default_speaker(self, name: str,dir: str): name = name.lower().strip() speaker_path=self.get_speaker_path(name,dir) return self.load_speaker(speaker_path) def create_prompt(self,text,lang,speaker_name=None): assert lang in ["hausa","igbo","yoruba","english"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba','english']" #if no speaker dir=self.DEFAULT_SPEAKERS_DIR_LOCAL if speaker_name is None: if lang=="hausa": speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"]) elif lang=="igbo": speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"]) elif lang=="yoruba": speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"]) else: speaker_name=random.choice(self.speakers_eng) if lang=="english": dir=self.DEFAULT_SPEAKERS_ENG speaker=self.load_default_speaker(speaker_name,dir) input_words = self.process_text(speaker["text"]) + self.process_text(text) #input_words = process_text(speaker["text"]) + input_words inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words]) prompt = self.text_prompt.format( bos=self.bos, text_start=self.special_tokens['text_start'], words=inputs_words_strings, text_end=self.special_tokens['text_end'], lang=self.special_tokens[lang], audio_start=self.special_tokens['audio_start'] ) prompt += self.create_audio_prompt(speaker["words"]) return prompt def replace_tokens(text): for pair in self.changed_tokens: text=text.replace(pair[0],pair[-1]) return text def resample(self,audio: np.ndarray, sr: int, target_sr: int): audio = audio.to(dtype=torch.float32) #.clone().detach() audio = audio.unsqueeze(0) # 1 as last arg corresponds to mono audio resampled = convert_audio(audio, sr, target_sr, 1) return resampled.to(self.device ) def quantize_wavtokenizer(self, path): audio_data, sample_rate = torchaudio.load(path) audio_data=audio_data.squeeze() audio = self.resample(audio_data, sample_rate, 24000).to(self.device) bandwidth_id = torch.tensor([0]).to(self.device ) _, codes = self.wavtokenizer.encode_infer(audio, bandwidth_id=bandwidth_id) codes = codes.squeeze(1).to(self.device)#+last_text_token res="" for code in codes[0].tolist(): res+=f"<|{code}|>" return res def load_asr_prompt(self,audio_path): codes=self.quantize_wavtokenizer(audio_path) prompt = self.asr_prompt.format( bos=self.bos, code_start=self.special_tokens['code_start'], codes=codes, code_end=self.special_tokens['code_end'], asr=self.special_tokens["asr"], ) return prompt def get_asr_results(self,output): res="" for text in self.tokenizer.decode(output[0]).split("<|text_start|>")[-1].split("<|text_end|>")[0].split("\n"): res+=text.split("<|word_start|>")[-1].split("<|word_end|>")[0] res+=" " return res.strip() ================================================ FILE: python-wrapper/default_speakers/.ipynb_checkpoints/Yoruba_prepare_data_naij (2)-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Rxa73RyKnhy3", "outputId": "aa525021-8667-4b2a-b879-f843eee12d7c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting outetts\n", " Downloading outetts-0.2.3-py3-none-any.whl.metadata (10 kB)\n", "Collecting uroman\n", " Downloading uroman-1.3.1.1-py3-none-any.whl.metadata (18 kB)\n", 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sha256=006ff9fec7048daea667dce09ad11d66d09d97d5e27939e2f27c96fd3223ab05\n", " Stored in directory: /root/.cache/pip/wheels/dd/4a/1f/d1c4bf3b9133c8168fe617ed979cab7b14fe381d059ffb9d83\n", " Building wheel for encodec (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for encodec: filename=encodec-0.1.1-py3-none-any.whl size=45760 sha256=451b0ff87f503b1e3e80ee75873ae179f23b53b055ffcac6e5414d3bdf11dad3\n", " Stored in directory: /root/.cache/pip/wheels/fc/36/cb/81af8b985a5f5e0815312d5e52b41263237af07b977e6bcbf3\n" ] } ], "source": [ "pip install outetts uroman noisereduce mecab-python3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HgJjekSOT8iX" }, "outputs": [], "source": [ "!pip install datasets triton snac wandb accelerate torchdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m4uPM3IpnsEo" }, "outputs": [], "source": [ "from outetts.wav_tokenizer.decoder import WavTokenizer\n", "from outetts.wav_tokenizer.encoder.utils import convert_audio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "543a-ZmC7xjE" }, "outputs": [], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EVyBedbQUM3F" }, "outputs": [], "source": [ "import torch\n", "import time\n", "import numpy as np\n", "import torchaudio\n", "from snac import SNAC\n", "from tqdm import tqdm\n", "import huggingface_hub\n", "import shutil\n", "import soundfile as sf\n", "from torch.utils.data import DataLoader, Dataset\n", "from transformers import AdamW, get_linear_schedule_with_warmup\n", "from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z8LFkziTgFRf" }, "outputs": [], "source": [ "import torchaudio\n", "import torch\n", "import torchaudio.functional as F\n", "import inflect\n", "import re\n", "import uroman as ur" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-wARjdSEUdjy" }, "outputs": [], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IYyt-dhuWx9q" }, "outputs": [], "source": [ "config_path = \"/content/drive/MyDrive/audio_datasets/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\"\n", "model_path = \"/content/drive/MyDrive/audio_datasets/wavtokenizer_large_speech_320_24k.ckpt\"#\"/content/wavtokenizer_medium_speech_320_24k_v2.ckpt\"\n", "wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)\n", "wavtokenizer = wavtokenizer.to(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TrfYeoWNV6T9" }, "outputs": [], "source": [ "class CTCForcedAlignment:\n", "\n", " def __init__(self, device: str = None):\n", " self.device = torch.device(device if device is not None else \"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " bundle = torchaudio.pipelines.MMS_FA\n", " self.sample_rate = bundle.sample_rate\n", " self.model = bundle.get_model(with_star=False).to(self.device)\n", " self.LABELS = bundle.get_labels(star=None)\n", " self.DICTIONARY = bundle.get_dict(star=None)\n", " self.lec = inflect.engine()\n", " self.uroman = ur.Uroman()\n", " #self.wakati = MeCab.Tagger(\"-Owakati\")\n", " #self.wakati_use = [\"ja\", \"zh\", \"ko\"]\n", " #self.languages = languages\n", "\n", " def process_text(self, text: str):\n", " #if language not in self.languages:\n", " # raise ValueError(f\"Language {language} not supported, supported languages are {self.languages}\")\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 _unflatten(self, list_, lengths):\n", " assert len(list_) == sum(lengths)\n", " i = 0\n", " ret = []\n", " for l in lengths:\n", " ret.append(list_[i : i + l])\n", " i += l\n", " return ret\n", "\n", " def get_word(self, waveform, spans, num_frames, transcript):\n", " ratio = waveform.size(1) / num_frames\n", " x0 = int(ratio * spans[0].start)\n", " x1 = int(ratio * spans[-1].end)\n", " return {\"x0\": x0, \"x1\": x1, \"word\": transcript}\n", "\n", " def _extract_world_level(self, aligned_tokens, alignment_scores, transcript):\n", " token_spans = F.merge_tokens(aligned_tokens, alignment_scores)\n", " word_spans = self._unflatten(token_spans, [len(word) for word in transcript])\n", " return word_spans\n", "\n", " def _align(self, emission, tokens):\n", " targets = torch.tensor([tokens], dtype=torch.int32, device=torch.device(\"cpu\"))\n", " alignments, scores = F.forced_align(emission.cpu(), targets, blank=0)\n", " alignments, scores = alignments[0], scores[0]\n", " scores = scores.exp()\n", " return alignments, scores\n", "\n", " def align(self, waveform,sr, transcript):\n", " #waveform, sr = torchaudio.load(audio)\n", " #waveform = torch.tensor(waveform)\n", " all_codes=quantize_wavtokenizer_ctc(waveform,sampling_rate=sr)\n", " if waveform.shape[0] > 1:\n", " waveform = waveform.mean(dim=0, keepdim=True)\n", " waveform = waveform.float()\n", " #print(waveform.shape)\n", " #print(sr)\n", " waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=self.sample_rate)\n", " transcript = self.process_text(transcript)\n", "\n", " with torch.inference_mode():\n", " emission, _ = self.model(waveform.to(self.device))\n", "\n", " tokenized_transcript = [self.DICTIONARY[c] for word in transcript for c in word]\n", " alignments, scores = self._align(emission, tokenized_transcript)\n", " word_spans = self._extract_world_level(alignments, scores, transcript)\n", " num_frames = emission.size(1)\n", "\n", " outputs = [\n", " self.get_word(waveform, word_spans[i], num_frames, transcript[i])\n", " for i in range(len(word_spans))\n", " ]\n", " #codes=quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000):\n", " #audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", "\n", " outputs[0][\"x0\"] = 0\n", " #print(waveform.shape)\n", " #print(self.sample_rate)\n", " for i in range(len(outputs)):\n", " output = outputs[i]\n", " x0 = output[\"x0\"]\n", "\n", " if i == len(outputs) - 1:\n", " x1 = output[\"x1\"]\n", " else:\n", " x1 = outputs[i + 1][\"x0\"]\n", " outputs[i][\"audio\"] = waveform[:, x0:x1]\n", " outputs[i][\"duration\"]=len(outputs[i][\"audio\"][0])/self.sample_rate\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", " #convert waveform to codes\n", " #duration Add audio\n", " return outputs\n", "\n", " def free(self):\n", " del self.model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CouG9BMIV6-K" }, "outputs": [], "source": [ "ctc = CTCForcedAlignment(\"cuda\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "68rBtr5GUcF2" }, "outputs": [], "source": [ "ctc.DICTIONARY" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "275g7SweCKAe" }, "outputs": [], "source": [ "def resample(audio: np.ndarray, sr: int, target_sr: int):\n", "\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(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "N85dYwCmWZG8" }, "outputs": [], "source": [ "def quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000, quantizer=wavtokenizer):\n", " #audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", " audio = resample(audio_data, sampling_rate, 24000).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio=audio.squeeze(0)\n", " _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n", " codes = codes.squeeze(1).to(device)#+last_text_token\n", "\n", " return codes[0].tolist()#+last_text_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QgGSndp8AoVW" }, "outputs": [], "source": [ "def resample(audio: np.ndarray, sr: int, target_sr: int):\n", "\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(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "txxV2uboCYih" }, "outputs": [], "source": [ "def quantize_wavtokenizer(row, quantizer=wavtokenizer):\n", " audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", " audio = resample(audio_data, sample_rate, 24000).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " #print(audio.shape)\n", " #print(audio.dim())\n", " _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n", " codes = codes.squeeze(1).to(device)#+last_text_token\n", "\n", " return codes[0].tolist()#+last_text_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JDfRH6HUIGiX" }, "outputs": [], "source": [ "def decode_tokenizer(discrete_code):\n", " #discrete code is a list\n", " discrete_code=torch.tensor([discrete_code]).to(device)-last_text_token\n", " features = wavtokenizer.codes_to_features(discrete_code).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n", " return audio_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0U_45AQey40V" }, "outputs": [], "source": [ "def decode_tokenizer(discrete_code):\n", " #discrete code is a list\n", " discrete_code=torch.tensor([[discrete_code]]).to(device)#-last_text_token\n", " features = wavtokenizer.codes_to_features(discrete_code).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n", " return audio_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ij19rZw-fEQ0" }, "outputs": [], "source": [ "class PromptProcessor():\n", " def __init__(self,lang):\n", " self.lang=lang\n", " self.bos = \"<|im_start|>\"\n", " self.eos = \"<|im_end|>\"\n", " self.tts_prompt = \"{bos}\\n{tts}\\n{text_start}{words}{text_end}\\n{lang}\\n{audio_start}\\n\"\n", " self.stt_prompt = \"{bos}\\n{stt}\\n{audio_start}{codes}{audio_end}\\n{lang}\\n{text_start}\\n\"\n", " self.special_tokens = {\n", " \"audio_code\": \"<|{}|>\",\n", " \"tts\":\"<|tts|>\",\n", " \"stt\":\"<|stt|>\",\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", " }\n", " super().__init__()\n", "\n", "\n", " def create_results_prompts(self,words):\n", " prompt_audio= []\n", " prompt_text=[]\n", " all_tokens=[]\n", " for i in words:\n", " word = i[\"word\"]\n", " duration = self.special_tokens[\"time\"].format(i[\"duration\"])\n", " tokens = \"\".join([self.special_tokens[\"audio_code\"].format(c) for c in i[\"codes\"]])\n", " all_tokens.append(tokens)\n", " prompt_audio.append(f'{word}{duration}{self.special_tokens[\"code_start\"]}{tokens}{self.special_tokens[\"code_end\"]}')\n", " prompt_text.append(f'{tokens}{duration}{self.special_tokens[\"word_start\"]}{word}{self.special_tokens[\"word_end\"]}')\n", " return \"\".join(all_tokens),\"\\n\".join(prompt_audio),\"\\n\".join(prompt_text)\n", "\n", "\n", "\n", " def get_prompt(self, row):\n", " try:\n", " audio=torch.from_numpy(row[\"audio\"][\"array\"]).unsqueeze(0)#torch.tensor([row[\"audio\"][\"array\"]])\n", " #print(audio)\n", " sample_rate=row[\"audio\"][\"sampling_rate\"]\n", " if row[\"text\"]:\n", " transcript=row[\"text\"]\n", " else:\n", " transcript=row[\"transcript\"]\n", " input_words = ctc.process_text(transcript)\n", " words= ctc.align(audio,sample_rate,transcript)\n", " #print(words)\n", " inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n", " #self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{audio_start}\\n\"\n", " prompt_tts= self.tts_prompt.format(\n", " bos=self.bos,\n", " text_start=self.special_tokens['text_start'],\n", " tts=self.special_tokens['tts'],\n", " words=inputs_words_strings,\n", " lang=self.special_tokens[self.lang],\n", " text_end=self.special_tokens['text_end'],\n", " audio_start=self.special_tokens['audio_start']\n", " )\n", "\n", "\n", " all_codes, tts_extra, stt_extra=self.create_results_prompts(words)\n", " prompt_stt=self.stt_prompt.format(\n", " bos=self.bos,\n", " audio_start=self.special_tokens['audio_start'],\n", " stt=self.special_tokens['stt'],\n", " codes=all_codes,\n", " lang=self.special_tokens[self.lang],\n", "\n", " audio_end=self.special_tokens['audio_end'],\n", " text_start=self.special_tokens['text_start']\n", " )\n", " prompt_stt+=stt_extra+f\"\\n{self.special_tokens['text_end']}\\n{self.eos}\\n\"\n", " prompt_tts+=tts_extra+f\"\\n{self.special_tokens['audio_end']}\\n{self.eos}\\n\"\n", "\n", " return {\"stt\":prompt_stt,\"tts\":prompt_tts}\n", " except Exception as e:\n", " #print(e)\n", " return {\"stt\":\"An error occurred\",\"tts\":\"An error occurred\"}#,\"An error occured\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ctohbEGTfZYq" }, "outputs": [], "source": [ "ps=PromptProcessor(\"yoruba\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "9f80b9ce82aa4c2bb3e6da8edb4887ef", "4d77ee1fa6ed43efa05683b12cf26239" ] }, "id": "Q7R28b7gd-9f", "outputId": "0c44d8ba-582f-42ca-f859-acb9a52a5729" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f80b9ce82aa4c2bb3e6da8edb4887ef", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
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], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 53 } ], "source": [ "ps.get_prompt(k)[\"tts\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "43YFGwbEbWkN" }, "outputs": [], "source": [ "data_yoruba = data_yoruba.cast_column(\"audio\", Audio(sampling_rate=24000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6aDesKzcQZQn", "outputId": "455497c1-814c-40f8-a6b4-c9812880aa96" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['audio', 'text'],\n", " num_rows: 15188\n", "})" ] }, "metadata": {}, "execution_count": 56 } ], "source": [ "data_yoruba" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bMOmeJx5IkAn" }, "outputs": [], "source": [ "start=0\n", "end=len(data_yoruba)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "--KPdDtTvVrN", "outputId": "b0a99ed0-cfb7-416b-ed7f-7f52fa778517" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "15188\n" ] } ], "source": [ "print(end)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZtcDBjbQh39V" }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 806, "referenced_widgets": [ "e0b88a0e362c4a6a90007d6dbb7898f7", "d43a756456da4d90b0ff3a68f495b2a4", "10bf93a19adb4be98db0eef6a6d3e4b7", "61fb2ad3726249e7997db481f16ec38d", "583a4e6780ae4b5fb57ff7a9abcbb8c0", "bf5d385efe034480a6094d60cabb0494", "76f5c621fdb842e884114096a5f39e2b", "073b1c763bd745f6988bb9bd801327c0", "885207f1cd3441ad8957327a2a982ac6", "d7b3312c66d849598a7043e3e73b4737", "89d909976ce94c08a91a5efacbd3e62e", "46d7d1c3a76243619f326cf8c7b73fca", 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sha256=451b0ff87f503b1e3e80ee75873ae179f23b53b055ffcac6e5414d3bdf11dad3\n", " Stored in directory: /root/.cache/pip/wheels/fc/36/cb/81af8b985a5f5e0815312d5e52b41263237af07b977e6bcbf3\n" ] } ], "source": [ "pip install outetts uroman noisereduce mecab-python3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HgJjekSOT8iX" }, "outputs": [], "source": [ "!pip install datasets triton snac wandb accelerate torchdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m4uPM3IpnsEo" }, "outputs": [], "source": [ "from outetts.wav_tokenizer.decoder import WavTokenizer\n", "from outetts.wav_tokenizer.encoder.utils import convert_audio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "543a-ZmC7xjE" }, "outputs": [], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EVyBedbQUM3F" }, "outputs": [], "source": [ "import torch\n", "import time\n", "import numpy as np\n", "import torchaudio\n", "from snac import SNAC\n", "from tqdm import tqdm\n", "import huggingface_hub\n", "import shutil\n", "import soundfile as sf\n", "from torch.utils.data import DataLoader, Dataset\n", "from transformers import AdamW, get_linear_schedule_with_warmup\n", "from datasets import load_dataset, concatenate_datasets, Audio, load_from_disk, interleave_datasets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z8LFkziTgFRf" }, "outputs": [], "source": [ "import torchaudio\n", "import torch\n", "import torchaudio.functional as F\n", "import inflect\n", "import re\n", "import uroman as ur" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-wARjdSEUdjy" }, "outputs": [], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IYyt-dhuWx9q" }, "outputs": [], "source": [ "config_path = \"/content/drive/MyDrive/audio_datasets/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\"\n", "model_path = \"/content/drive/MyDrive/audio_datasets/wavtokenizer_large_speech_320_24k.ckpt\"#\"/content/wavtokenizer_medium_speech_320_24k_v2.ckpt\"\n", "wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)\n", "wavtokenizer = wavtokenizer.to(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TrfYeoWNV6T9" }, "outputs": [], "source": [ "class CTCForcedAlignment:\n", "\n", " def __init__(self, device: str = None):\n", " self.device = torch.device(device if device is not None else \"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " bundle = torchaudio.pipelines.MMS_FA\n", " self.sample_rate = bundle.sample_rate\n", " self.model = bundle.get_model(with_star=False).to(self.device)\n", " self.LABELS = bundle.get_labels(star=None)\n", " self.DICTIONARY = bundle.get_dict(star=None)\n", " self.lec = inflect.engine()\n", " self.uroman = ur.Uroman()\n", " #self.wakati = MeCab.Tagger(\"-Owakati\")\n", " #self.wakati_use = [\"ja\", \"zh\", \"ko\"]\n", " #self.languages = languages\n", "\n", " def process_text(self, text: str):\n", " #if language not in self.languages:\n", " # raise ValueError(f\"Language {language} not supported, supported languages are {self.languages}\")\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 _unflatten(self, list_, lengths):\n", " assert len(list_) == sum(lengths)\n", " i = 0\n", " ret = []\n", " for l in lengths:\n", " ret.append(list_[i : i + l])\n", " i += l\n", " return ret\n", "\n", " def get_word(self, waveform, spans, num_frames, transcript):\n", " ratio = waveform.size(1) / num_frames\n", " x0 = int(ratio * spans[0].start)\n", " x1 = int(ratio * spans[-1].end)\n", " return {\"x0\": x0, \"x1\": x1, \"word\": transcript}\n", "\n", " def _extract_world_level(self, aligned_tokens, alignment_scores, transcript):\n", " token_spans = F.merge_tokens(aligned_tokens, alignment_scores)\n", " word_spans = self._unflatten(token_spans, [len(word) for word in transcript])\n", " return word_spans\n", "\n", " def _align(self, emission, tokens):\n", " targets = torch.tensor([tokens], dtype=torch.int32, device=torch.device(\"cpu\"))\n", " alignments, scores = F.forced_align(emission.cpu(), targets, blank=0)\n", " alignments, scores = alignments[0], scores[0]\n", " scores = scores.exp()\n", " return alignments, scores\n", "\n", " def align(self, waveform,sr, transcript):\n", " #waveform, sr = torchaudio.load(audio)\n", " #waveform = torch.tensor(waveform)\n", " all_codes=quantize_wavtokenizer_ctc(waveform,sampling_rate=sr)\n", " if waveform.shape[0] > 1:\n", " waveform = waveform.mean(dim=0, keepdim=True)\n", " waveform = waveform.float()\n", " #print(waveform.shape)\n", " #print(sr)\n", " waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=self.sample_rate)\n", " transcript = self.process_text(transcript)\n", "\n", " with torch.inference_mode():\n", " emission, _ = self.model(waveform.to(self.device))\n", "\n", " tokenized_transcript = [self.DICTIONARY[c] for word in transcript for c in word]\n", " alignments, scores = self._align(emission, tokenized_transcript)\n", " word_spans = self._extract_world_level(alignments, scores, transcript)\n", " num_frames = emission.size(1)\n", "\n", " outputs = [\n", " self.get_word(waveform, word_spans[i], num_frames, transcript[i])\n", " for i in range(len(word_spans))\n", " ]\n", " #codes=quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000):\n", " #audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", "\n", " outputs[0][\"x0\"] = 0\n", " #print(waveform.shape)\n", " #print(self.sample_rate)\n", " for i in range(len(outputs)):\n", " output = outputs[i]\n", " x0 = output[\"x0\"]\n", "\n", " if i == len(outputs) - 1:\n", " x1 = output[\"x1\"]\n", " else:\n", " x1 = outputs[i + 1][\"x0\"]\n", " outputs[i][\"audio\"] = waveform[:, x0:x1]\n", " outputs[i][\"duration\"]=len(outputs[i][\"audio\"][0])/self.sample_rate\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", " #convert waveform to codes\n", " #duration Add audio\n", " return outputs\n", "\n", " def free(self):\n", " del self.model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CouG9BMIV6-K" }, "outputs": [], "source": [ "ctc = CTCForcedAlignment(\"cuda\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "68rBtr5GUcF2" }, "outputs": [], "source": [ "ctc.DICTIONARY" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "275g7SweCKAe" }, "outputs": [], "source": [ "def resample(audio: np.ndarray, sr: int, target_sr: int):\n", "\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(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "N85dYwCmWZG8" }, "outputs": [], "source": [ "def quantize_wavtokenizer_ctc(audio_data,sampling_rate=16000, quantizer=wavtokenizer):\n", " #audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", " audio = resample(audio_data, sampling_rate, 24000).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio=audio.squeeze(0)\n", " _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n", " codes = codes.squeeze(1).to(device)#+last_text_token\n", "\n", " return codes[0].tolist()#+last_text_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QgGSndp8AoVW" }, "outputs": [], "source": [ "def resample(audio: np.ndarray, sr: int, target_sr: int):\n", "\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(device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "txxV2uboCYih" }, "outputs": [], "source": [ "def quantize_wavtokenizer(row, quantizer=wavtokenizer):\n", " audio_data, sample_rate = row[\"audio\"][\"array\"], int(row[\"audio\"][\"sampling_rate\"])\n", "\n", " audio = resample(audio_data, sample_rate, 24000).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " #print(audio.shape)\n", " #print(audio.dim())\n", " _, codes = quantizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n", " codes = codes.squeeze(1).to(device)#+last_text_token\n", "\n", " return codes[0].tolist()#+last_text_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JDfRH6HUIGiX" }, "outputs": [], "source": [ "def decode_tokenizer(discrete_code):\n", " #discrete code is a list\n", " discrete_code=torch.tensor([discrete_code]).to(device)-last_text_token\n", " features = wavtokenizer.codes_to_features(discrete_code).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n", " return audio_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0U_45AQey40V" }, "outputs": [], "source": [ "def decode_tokenizer(discrete_code):\n", " #discrete code is a list\n", " discrete_code=torch.tensor([[discrete_code]]).to(device)#-last_text_token\n", " features = wavtokenizer.codes_to_features(discrete_code).to(device)\n", " bandwidth_id = torch.tensor([0]).to(device)\n", " audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)\n", " return audio_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ij19rZw-fEQ0" }, "outputs": [], "source": [ "class PromptProcessor():\n", " def __init__(self,lang):\n", " self.lang=lang\n", " self.bos = \"<|im_start|>\"\n", " self.eos = \"<|im_end|>\"\n", " self.tts_prompt = \"{bos}\\n{tts}\\n{text_start}{words}{text_end}\\n{lang}\\n{audio_start}\\n\"\n", " self.stt_prompt = \"{bos}\\n{stt}\\n{audio_start}{codes}{audio_end}\\n{lang}\\n{text_start}\\n\"\n", " self.special_tokens = {\n", " \"audio_code\": \"<|{}|>\",\n", " \"tts\":\"<|tts|>\",\n", " \"stt\":\"<|stt|>\",\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", " }\n", " super().__init__()\n", "\n", "\n", " def create_results_prompts(self,words):\n", " prompt_audio= []\n", " prompt_text=[]\n", " all_tokens=[]\n", " for i in words:\n", " word = i[\"word\"]\n", " duration = self.special_tokens[\"time\"].format(i[\"duration\"])\n", " tokens = \"\".join([self.special_tokens[\"audio_code\"].format(c) for c in i[\"codes\"]])\n", " all_tokens.append(tokens)\n", " prompt_audio.append(f'{word}{duration}{self.special_tokens[\"code_start\"]}{tokens}{self.special_tokens[\"code_end\"]}')\n", " prompt_text.append(f'{tokens}{duration}{self.special_tokens[\"word_start\"]}{word}{self.special_tokens[\"word_end\"]}')\n", " return \"\".join(all_tokens),\"\\n\".join(prompt_audio),\"\\n\".join(prompt_text)\n", "\n", "\n", "\n", " def get_prompt(self, row):\n", " try:\n", " audio=torch.from_numpy(row[\"audio\"][\"array\"]).unsqueeze(0)#torch.tensor([row[\"audio\"][\"array\"]])\n", " #print(audio)\n", " sample_rate=row[\"audio\"][\"sampling_rate\"]\n", " if row[\"text\"]:\n", " transcript=row[\"text\"]\n", " else:\n", " transcript=row[\"transcript\"]\n", " input_words = ctc.process_text(transcript)\n", " words= ctc.align(audio,sample_rate,transcript)\n", " #print(words)\n", " inputs_words_strings = f\"{self.special_tokens['text_sep']}\".join([i.strip() for i in input_words])\n", " #self.text_prompt = \"{bos}\\n{text_start}{words}{text_end}\\n{audio_start}\\n\"\n", " prompt_tts= self.tts_prompt.format(\n", " bos=self.bos,\n", " text_start=self.special_tokens['text_start'],\n", " tts=self.special_tokens['tts'],\n", " words=inputs_words_strings,\n", " lang=self.special_tokens[self.lang],\n", " text_end=self.special_tokens['text_end'],\n", " audio_start=self.special_tokens['audio_start']\n", " )\n", "\n", "\n", " all_codes, tts_extra, stt_extra=self.create_results_prompts(words)\n", " prompt_stt=self.stt_prompt.format(\n", " bos=self.bos,\n", " audio_start=self.special_tokens['audio_start'],\n", " stt=self.special_tokens['stt'],\n", " codes=all_codes,\n", " lang=self.special_tokens[self.lang],\n", "\n", " audio_end=self.special_tokens['audio_end'],\n", " text_start=self.special_tokens['text_start']\n", " )\n", " prompt_stt+=stt_extra+f\"\\n{self.special_tokens['text_end']}\\n{self.eos}\\n\"\n", " prompt_tts+=tts_extra+f\"\\n{self.special_tokens['audio_end']}\\n{self.eos}\\n\"\n", "\n", " return {\"stt\":prompt_stt,\"tts\":prompt_tts}\n", " except Exception as e:\n", " #print(e)\n", " return {\"stt\":\"An error occurred\",\"tts\":\"An error occurred\"}#,\"An error occured\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ctohbEGTfZYq" }, "outputs": [], "source": [ "ps=PromptProcessor(\"yoruba\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "9f80b9ce82aa4c2bb3e6da8edb4887ef", "4d77ee1fa6ed43efa05683b12cf26239" ] }, "id": "Q7R28b7gd-9f", "outputId": "0c44d8ba-582f-42ca-f859-acb9a52a5729" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f80b9ce82aa4c2bb3e6da8edb4887ef", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
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] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ps.get_prompt(k)[\"tts\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "43YFGwbEbWkN" }, "outputs": [], "source": [ "data_yoruba = data_yoruba.cast_column(\"audio\", Audio(sampling_rate=24000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6aDesKzcQZQn", "outputId": "455497c1-814c-40f8-a6b4-c9812880aa96" }, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['audio', 'text'],\n", " num_rows: 15188\n", "})" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_yoruba" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bMOmeJx5IkAn" }, "outputs": [], "source": [ "start=0\n", "end=len(data_yoruba)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "--KPdDtTvVrN", "outputId": "b0a99ed0-cfb7-416b-ed7f-7f52fa778517" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15188\n" ] } ], "source": [ "print(end)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZtcDBjbQh39V" }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 806, "referenced_widgets": [ "e0b88a0e362c4a6a90007d6dbb7898f7", "d43a756456da4d90b0ff3a68f495b2a4", "10bf93a19adb4be98db0eef6a6d3e4b7", "61fb2ad3726249e7997db481f16ec38d", "583a4e6780ae4b5fb57ff7a9abcbb8c0", "bf5d385efe034480a6094d60cabb0494", "76f5c621fdb842e884114096a5f39e2b", "073b1c763bd745f6988bb9bd801327c0", "885207f1cd3441ad8957327a2a982ac6", "d7b3312c66d849598a7043e3e73b4737", "89d909976ce94c08a91a5efacbd3e62e", "46d7d1c3a76243619f326cf8c7b73fca", "54bba0e876be46dda328603faa8cf66e", 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================================================ FILE: python-wrapper/default_speakers/tayo.json ================================================ { "text": "and enjoy ourselves we need more parties let party start again now we know", "words": [ { "word": "and", "duration": 0.5, "codes": [ 82, 1201, 329, 992, 908, 847, 925, 1666, 1057, 1266, 1448, 1737, 1251, 1031, 1759, 1459, 1094, 1750, 1739, 1521, 594, 1625, 732, 1326, 1095, 828, 239, 752, 1221, 1382, 705, 1716, 865, 1503, 478, 1692, 938 ] }, { "word": "enjoy", "duration": 0.4, "codes": [ 844, 192, 737, 344, 276, 138, 48, 1616, 28, 1530, 1550, 1383, 1712, 69, 1261, 547, 249, 1047, 500, 182, 63, 1445, 935, 865, 1478, 1670, 479, 116, 1674, 886 ] }, { "word": "ourselves", "duration": 0.7, "codes": [ 467, 1534, 901, 569, 1740, 882, 1579, 507, 276, 1296, 543, 399, 404, 1624, 1666, 153, 102, 1323, 1552, 65, 898, 1577, 757, 1446, 1022, 363, 124, 947, 1441, 581, 1677, 1269, 1525, 1170, 505, 1681, 1212, 1273, 1364, 1513, 1826, 1139, 1756, 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1308 ] }, { "word": "all", "duration": 0.42, "codes": [ 863, 610, 429, 443, 1087, 183, 782, 613, 222, 1047, 1492, 154, 955, 429, 443, 613, 983, 328, 382, 359, 341, 217, 456, 289, 1324, 714, 756, 369, 211, 127, 1827, 1563 ] }, { "word": "the", "duration": 0.12, "codes": [ 1686, 949, 1296, 829, 1463, 1731, 1222, 1353, 1780 ] }, { "word": "way", "duration": 0.18, "codes": [ 1263, 890, 683, 289, 217, 326, 335, 1059, 1204, 213, 1340, 289, 191 ] } ] } ================================================ FILE: python-wrapper/pyproject.toml ================================================ [build-system] requires = ["setuptools>=42", "wheel"] build-backend = "setuptools.build_meta" [project] name = "yarngpt" version = "0.1.5" description = "A Python wrapper for YarnGPT text-to-speech model" readme = "README.md" requires-python = ">=3.6" license = { file = "LICENSE" } authors = [ { name = "Abayomi Olagunju", email = "olagunjujeremiah@gmail.com" } ] urls = { "Homepage" = "https://github.com/jerryola1" } dependencies = [ "torch", "transformers", "torchaudio", "outetts==0.2.3", "uroman", "numpy", "inflect", "IPython", "tqdm" ] classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License" ] [tool.setuptools.packages.find] where = ["."] include = ["yarngpt", "yarngpt.*", "default_speakers"] exclude = [ "models", "dist", "env", "testenv", "tests", "__pycache__", "yarngpt.egg-info", "freshenv" ] [tool.setuptools] py-modules = ["audiotokenizer"] [tool.setuptools.package-data] default_speakers = ["*.json"] ================================================ FILE: python-wrapper/requirements.txt ================================================ torch transformers torchaudio outetts==0.2.3 uroman numpy inflect IPython build tqdm ================================================ FILE: python-wrapper/yarngpt/__init__.py ================================================ from yarngpt.core import generate_speech __version__ = "0.1.5" __all__ = ["generate_speech"] ================================================ FILE: python-wrapper/yarngpt/core.py ================================================ import os import torch import requests from transformers import AutoModelForCausalLM from audiotokenizer import AudioTokenizer from tqdm import tqdm #define model storage directory MODEL_DIR = os.path.expanduser("~/.yarngpt/models") os.makedirs(MODEL_DIR, exist_ok=True) #define file paths CONFIG_PATH = os.path.join(MODEL_DIR, "wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml") MODEL_PATH = os.path.join(MODEL_DIR, "wavtokenizer_large_speech_320_24k.ckpt") #urls from Hugging Face CONFIG_URL = "https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml" MODEL_URL = "https://huggingface.co/novateur/WavTokenizer-large-speech-75token/resolve/main/wavtokenizer_large_speech_320_24k.ckpt" def download_file(url, dest_path): """Downloads a file with a progress bar if it doesn't already exist.""" if os.path.exists(dest_path): print(f"{dest_path} already exists. Skipping download.") return print(f"Downloading {url} to {dest_path}...") response = requests.get(url, stream=True) total_size = int(response.headers.get('content-length', 0)) with open(dest_path, "wb") as f, tqdm( total=total_size, unit="B", unit_scale=True, desc=os.path.basename(dest_path) ) as progress_bar: for chunk in response.iter_content(chunk_size=8192): f.write(chunk) progress_bar.update(len(chunk)) print("Download complete.") #ensure model files are available download_file(CONFIG_URL, CONFIG_PATH) download_file(MODEL_URL, MODEL_PATH) #list of available speakers AVAILABLE_SPEAKERS = [ "idera", "jude", "joke", "umar", "osagie", "onye" ] def load_model_and_tokenizer(): """Loads the YarnGPT model and tokenizer.""" hf_path = "saheedniyi/YarnGPT" #initialize tokenizer audio_tokenizer = AudioTokenizer(hf_path, MODEL_PATH, CONFIG_PATH) #load model using Hugging Face's caching system model = AutoModelForCausalLM.from_pretrained(hf_path, torch_dtype="auto") model = model.to(audio_tokenizer.device) return model, audio_tokenizer def generate_speech(text, speaker="idera", temperature=0.1, repetition_penalty=1.1, max_length=4000): """Generate speech audio from input text using the selected speaker. This function converts text to speech using YarnGPT's text-to-speech model with Nigerian-accented English. It supports multiple preset voices and allows customization of generation parameters. Args: text (str): The input text to convert to speech. speaker (str, optional): The voice to use for speech generation. Must be one of: idera, jude, joke, umar, osagie, onye. Defaults to "idera". temperature (float, optional): Controls randomness in generation. Higher values (e.g., 0.8) make output more random, lower values (e.g., 0.1) make it more deterministic. Defaults to 0.1. repetition_penalty (float, optional): Penalizes repetition in generated speech. Values > 1.0 reduce repetition. Defaults to 1.1. max_length (int, optional): Maximum length of generated sequence. Longer text needs higher values. Defaults to 4000. Returns: torch.Tensor: A 2D tensor containing the generated audio waveform with shape (1, num_samples) and sample rate of 24kHz. Raises: ValueError: If speaker is not one of the available preset voices. Example: >>> from yarngpt import generate_speech >>> import torchaudio >>> >>> # Generate speech with default settings >>> audio = generate_speech("Hello, how are you?") >>> >>> # Save the generated audio >>> torchaudio.save("output.wav", audio, sample_rate=24000) >>> >>> # Use a different speaker with custom parameters >>> audio = generate_speech( ... "This is a test.", ... speaker="joke", ... temperature=0.2, ... repetition_penalty=1.2 ... ) """ if speaker not in AVAILABLE_SPEAKERS: raise ValueError(f"Speaker must be one of: {', '.join(AVAILABLE_SPEAKERS)}") model, audio_tokenizer = load_model_and_tokenizer() prompt = audio_tokenizer.create_prompt(text, speaker) input_ids = audio_tokenizer.tokenize_prompt(prompt) output = model.generate( input_ids=input_ids, temperature=temperature, repetition_penalty=repetition_penalty, max_length=max_length ) codes = audio_tokenizer.get_codes(output) audio = audio_tokenizer.get_audio(codes) return audio ================================================ FILE: requirements.txt ================================================ outetts==0.2.3 uroman torch torchaudio transformers==4.47.1 inflect