Full Code of SesameAILabs/csm for AI

Repository: SesameAILabs/csm
Branch: main
Commit: daed31e6d42c
Files: 9
Total size: 37.7 KB

Directory structure:
gitextract_as73dgax/

├── .gitignore
├── LICENSE
├── README.md
├── generator.py
├── models.py
├── requirements.txt
├── run_csm.py
├── setup.py
└── watermarking.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg

# Virtual Environment
.env
.venv
env/
venv/
ENV/

# IDE
.idea/
.vscode/
*.swp
*.swo

# Project specific
.python-version
*.wav
output_*/
basic_audio.wav
full_conversation.wav
context_audio.wav

# Model files
*.pt
*.ckpt

================================================
FILE: LICENSE
================================================
                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.


================================================
FILE: README.md
================================================
# CSM

**2025/05/20** - CSM is availabile natively in [Hugging Face Transformers](https://huggingface.co/docs/transformers/main/en/model_doc/csm) 🤗 as of version `4.52.1`, more info available [in our model repo](https://huggingface.co/sesame/csm-1b)

**2025/03/13** - We are releasing the 1B CSM variant. The checkpoint is [hosted on Hugging Face](https://huggingface.co/sesame/csm_1b).

---

CSM (Conversational Speech Model) is a speech generation model from [Sesame](https://www.sesame.com) that generates RVQ audio codes from text and audio inputs. The model architecture employs a [Llama](https://www.llama.com/) backbone and a smaller audio decoder that produces [Mimi](https://huggingface.co/kyutai/mimi) audio codes.

A fine-tuned variant of CSM powers the [interactive voice demo](https://www.sesame.com/voicedemo) shown in our [blog post](https://www.sesame.com/research/crossing_the_uncanny_valley_of_voice).

A hosted [Hugging Face space](https://huggingface.co/spaces/sesame/csm-1b) is also available for testing audio generation.

## Requirements

* A CUDA-compatible GPU
* The code has been tested on CUDA 12.4 and 12.6, but it may also work on other versions
* Similarly, Python 3.10 is recommended, but newer versions may be fine
* For some audio operations, `ffmpeg` may be required
* Access to the following Hugging Face models:
  * [Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)
  * [CSM-1B](https://huggingface.co/sesame/csm-1b)

### Setup

```bash
git clone git@github.com:SesameAILabs/csm.git
cd csm
python3.10 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Disable lazy compilation in Mimi
export NO_TORCH_COMPILE=1

# You will need access to CSM-1B and Llama-3.2-1B
huggingface-cli login
```

### Windows Setup

The `triton` package cannot be installed in Windows. Instead use `pip install triton-windows`.

## Quickstart

This script will generate a conversation between 2 characters, using a prompt for each character.

```bash
python run_csm.py
```

## Usage

If you want to write your own applications with CSM, the following examples show basic usage.

#### Generate a sentence

This will use a random speaker identity, as no prompt or context is provided.

```python
from generator import load_csm_1b
import torchaudio
import torch

if torch.backends.mps.is_available():
    device = "mps"
elif torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

generator = load_csm_1b(device=device)

audio = generator.generate(
    text="Hello from Sesame.",
    speaker=0,
    context=[],
    max_audio_length_ms=10_000,
)

torchaudio.save("audio.wav", audio.unsqueeze(0).cpu(), generator.sample_rate)
```

#### Generate with context

CSM sounds best when provided with context. You can prompt or provide context to the model using a `Segment` for each speaker's utterance.

NOTE: The following example is instructional and the audio files do not exist. It is intended as an example for using context with CSM.

```python
from generator import Segment

speakers = [0, 1, 0, 0]
transcripts = [
    "Hey how are you doing.",
    "Pretty good, pretty good.",
    "I'm great.",
    "So happy to be speaking to you.",
]
audio_paths = [
    "utterance_0.wav",
    "utterance_1.wav",
    "utterance_2.wav",
    "utterance_3.wav",
]

def load_audio(audio_path):
    audio_tensor, sample_rate = torchaudio.load(audio_path)
    audio_tensor = torchaudio.functional.resample(
        audio_tensor.squeeze(0), orig_freq=sample_rate, new_freq=generator.sample_rate
    )
    return audio_tensor

segments = [
    Segment(text=transcript, speaker=speaker, audio=load_audio(audio_path))
    for transcript, speaker, audio_path in zip(transcripts, speakers, audio_paths)
]
audio = generator.generate(
    text="Me too, this is some cool stuff huh?",
    speaker=1,
    context=segments,
    max_audio_length_ms=10_000,
)

torchaudio.save("audio.wav", audio.unsqueeze(0).cpu(), generator.sample_rate)
```

## FAQ

**Does this model come with any voices?**

The model open-sourced here is a base generation model. It is capable of producing a variety of voices, but it has not been fine-tuned on any specific voice.

**Can I converse with the model?**

CSM is trained to be an audio generation model and not a general-purpose multimodal LLM. It cannot generate text. We suggest using a separate LLM for text generation.

**Does it support other languages?**

The model has some capacity for non-English languages due to data contamination in the training data, but it likely won't do well.

## Misuse and abuse ⚠️

This project provides a high-quality speech generation model for research and educational purposes. While we encourage responsible and ethical use, we **explicitly prohibit** the following:

- **Impersonation or Fraud**: Do not use this model to generate speech that mimics real individuals without their explicit consent.
- **Misinformation or Deception**: Do not use this model to create deceptive or misleading content, such as fake news or fraudulent calls.
- **Illegal or Harmful Activities**: Do not use this model for any illegal, harmful, or malicious purposes.

By using this model, you agree to comply with all applicable laws and ethical guidelines. We are **not responsible** for any misuse, and we strongly condemn unethical applications of this technology.

---

## Authors
Johan Schalkwyk, Ankit Kumar, Dan Lyth, Sefik Emre Eskimez, Zack Hodari, Cinjon Resnick, Ramon Sanabria, Raven Jiang, and the Sesame team.


================================================
FILE: generator.py
================================================
from dataclasses import dataclass
from typing import List, Tuple

import torch
import torchaudio
from huggingface_hub import hf_hub_download
from models import Model
from moshi.models import loaders
from tokenizers.processors import TemplateProcessing
from transformers import AutoTokenizer
from watermarking import CSM_1B_GH_WATERMARK, load_watermarker, watermark


@dataclass
class Segment:
    speaker: int
    text: str
    # (num_samples,), sample_rate = 24_000
    audio: torch.Tensor


def load_llama3_tokenizer():
    """
    https://github.com/huggingface/transformers/issues/22794#issuecomment-2092623992
    """
    tokenizer_name = "meta-llama/Llama-3.2-1B"
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
    bos = tokenizer.bos_token
    eos = tokenizer.eos_token
    tokenizer._tokenizer.post_processor = TemplateProcessing(
        single=f"{bos}:0 $A:0 {eos}:0",
        pair=f"{bos}:0 $A:0 {eos}:0 {bos}:1 $B:1 {eos}:1",
        special_tokens=[(f"{bos}", tokenizer.bos_token_id), (f"{eos}", tokenizer.eos_token_id)],
    )

    return tokenizer


class Generator:
    def __init__(
        self,
        model: Model,
    ):
        self._model = model
        self._model.setup_caches(1)

        self._text_tokenizer = load_llama3_tokenizer()

        device = next(model.parameters()).device
        mimi_weight = hf_hub_download(loaders.DEFAULT_REPO, loaders.MIMI_NAME)
        mimi = loaders.get_mimi(mimi_weight, device=device)
        mimi.set_num_codebooks(32)
        self._audio_tokenizer = mimi

        self._watermarker = load_watermarker(device=device)

        self.sample_rate = mimi.sample_rate
        self.device = device

    def _tokenize_text_segment(self, text: str, speaker: int) -> Tuple[torch.Tensor, torch.Tensor]:
        frame_tokens = []
        frame_masks = []

        text_tokens = self._text_tokenizer.encode(f"[{speaker}]{text}")
        text_frame = torch.zeros(len(text_tokens), 33).long()
        text_frame_mask = torch.zeros(len(text_tokens), 33).bool()
        text_frame[:, -1] = torch.tensor(text_tokens)
        text_frame_mask[:, -1] = True

        frame_tokens.append(text_frame.to(self.device))
        frame_masks.append(text_frame_mask.to(self.device))

        return torch.cat(frame_tokens, dim=0), torch.cat(frame_masks, dim=0)

    def _tokenize_audio(self, audio: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        assert audio.ndim == 1, "Audio must be single channel"

        frame_tokens = []
        frame_masks = []

        # (K, T)
        audio = audio.to(self.device)
        audio_tokens = self._audio_tokenizer.encode(audio.unsqueeze(0).unsqueeze(0))[0]
        # add EOS frame
        eos_frame = torch.zeros(audio_tokens.size(0), 1).to(self.device)
        audio_tokens = torch.cat([audio_tokens, eos_frame], dim=1)

        audio_frame = torch.zeros(audio_tokens.size(1), 33).long().to(self.device)
        audio_frame_mask = torch.zeros(audio_tokens.size(1), 33).bool().to(self.device)
        audio_frame[:, :-1] = audio_tokens.transpose(0, 1)
        audio_frame_mask[:, :-1] = True

        frame_tokens.append(audio_frame)
        frame_masks.append(audio_frame_mask)

        return torch.cat(frame_tokens, dim=0), torch.cat(frame_masks, dim=0)

    def _tokenize_segment(self, segment: Segment) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Returns:
            (seq_len, 33), (seq_len, 33)
        """
        text_tokens, text_masks = self._tokenize_text_segment(segment.text, segment.speaker)
        audio_tokens, audio_masks = self._tokenize_audio(segment.audio)

        return torch.cat([text_tokens, audio_tokens], dim=0), torch.cat([text_masks, audio_masks], dim=0)

    @torch.inference_mode()
    def generate(
        self,
        text: str,
        speaker: int,
        context: List[Segment],
        max_audio_length_ms: float = 90_000,
        temperature: float = 0.9,
        topk: int = 50,
    ) -> torch.Tensor:
        self._model.reset_caches()

        max_generation_len = int(max_audio_length_ms / 80)
        tokens, tokens_mask = [], []
        for segment in context:
            segment_tokens, segment_tokens_mask = self._tokenize_segment(segment)
            tokens.append(segment_tokens)
            tokens_mask.append(segment_tokens_mask)

        gen_segment_tokens, gen_segment_tokens_mask = self._tokenize_text_segment(text, speaker)
        tokens.append(gen_segment_tokens)
        tokens_mask.append(gen_segment_tokens_mask)

        prompt_tokens = torch.cat(tokens, dim=0).long().to(self.device)
        prompt_tokens_mask = torch.cat(tokens_mask, dim=0).bool().to(self.device)

        samples = []
        curr_tokens = prompt_tokens.unsqueeze(0)
        curr_tokens_mask = prompt_tokens_mask.unsqueeze(0)
        curr_pos = torch.arange(0, prompt_tokens.size(0)).unsqueeze(0).long().to(self.device)

        max_seq_len = 2048
        max_context_len = max_seq_len - max_generation_len
        if curr_tokens.size(1) >= max_context_len:
            raise ValueError(
                f"Inputs too long, must be below max_seq_len - max_generation_len: {max_context_len}"
            )

        for _ in range(max_generation_len):
            sample = self._model.generate_frame(curr_tokens, curr_tokens_mask, curr_pos, temperature, topk)
            if torch.all(sample == 0):
                break  # eos

            samples.append(sample)

            curr_tokens = torch.cat([sample, torch.zeros(1, 1).long().to(self.device)], dim=1).unsqueeze(1)
            curr_tokens_mask = torch.cat(
                [torch.ones_like(sample).bool(), torch.zeros(1, 1).bool().to(self.device)], dim=1
            ).unsqueeze(1)
            curr_pos = curr_pos[:, -1:] + 1

        audio = self._audio_tokenizer.decode(torch.stack(samples).permute(1, 2, 0)).squeeze(0).squeeze(0)

        # This applies an imperceptible watermark to identify audio as AI-generated.
        # Watermarking ensures transparency, dissuades misuse, and enables traceability.
        # Please be a responsible AI citizen and keep the watermarking in place.
        # If using CSM 1B in another application, use your own private key and keep it secret.
        audio, wm_sample_rate = watermark(self._watermarker, audio, self.sample_rate, CSM_1B_GH_WATERMARK)
        audio = torchaudio.functional.resample(audio, orig_freq=wm_sample_rate, new_freq=self.sample_rate)

        return audio


def load_csm_1b(device: str = "cuda") -> Generator:
    model = Model.from_pretrained("sesame/csm-1b")
    model.to(device=device, dtype=torch.bfloat16)

    generator = Generator(model)
    return generator

================================================
FILE: models.py
================================================
from dataclasses import dataclass

import torch
import torch.nn as nn
import torchtune
from huggingface_hub import PyTorchModelHubMixin
from torchtune.models import llama3_2


def llama3_2_1B() -> torchtune.modules.transformer.TransformerDecoder:
    return llama3_2.llama3_2(
        vocab_size=128_256,
        num_layers=16,
        num_heads=32,
        num_kv_heads=8,
        embed_dim=2048,
        max_seq_len=2048,
        intermediate_dim=8192,
        attn_dropout=0.0,
        norm_eps=1e-5,
        rope_base=500_000,
        scale_factor=32,
    )


def llama3_2_100M() -> torchtune.modules.transformer.TransformerDecoder:
    return llama3_2.llama3_2(
        vocab_size=128_256,
        num_layers=4,
        num_heads=8,
        num_kv_heads=2,
        embed_dim=1024,
        max_seq_len=2048,
        intermediate_dim=8192,
        attn_dropout=0.0,
        norm_eps=1e-5,
        rope_base=500_000,
        scale_factor=32,
    )


FLAVORS = {
    "llama-1B": llama3_2_1B,
    "llama-100M": llama3_2_100M,
}


def _prepare_transformer(model):
    embed_dim = model.tok_embeddings.embedding_dim
    model.tok_embeddings = nn.Identity()
    model.output = nn.Identity()
    return model, embed_dim


def _create_causal_mask(seq_len: int, device: torch.device):
    return torch.tril(torch.ones(seq_len, seq_len, dtype=torch.bool, device=device))


def _index_causal_mask(mask: torch.Tensor, input_pos: torch.Tensor):
    """
    Args:
        mask: (max_seq_len, max_seq_len)
        input_pos: (batch_size, seq_len)

    Returns:
        (batch_size, seq_len, max_seq_len)
    """
    r = mask[input_pos, :]
    return r


def _multinomial_sample_one_no_sync(probs):  # Does multinomial sampling without a cuda synchronization
    q = torch.empty_like(probs).exponential_(1)
    return torch.argmax(probs / q, dim=-1, keepdim=True).to(dtype=torch.int)


def sample_topk(logits: torch.Tensor, topk: int, temperature: float):
    logits = logits / temperature

    filter_value: float = -float("Inf")
    indices_to_remove = logits < torch.topk(logits, topk)[0][..., -1, None]
    scores_processed = logits.masked_fill(indices_to_remove, filter_value)
    scores_processed = torch.nn.functional.log_softmax(scores_processed, dim=-1)
    probs = torch.nn.functional.softmax(scores_processed, dim=-1)

    sample_token = _multinomial_sample_one_no_sync(probs)
    return sample_token


@dataclass
class ModelArgs:
    backbone_flavor: str
    decoder_flavor: str
    text_vocab_size: int
    audio_vocab_size: int
    audio_num_codebooks: int


class Model(
    nn.Module,
    PyTorchModelHubMixin,
    repo_url="https://github.com/SesameAILabs/csm",
    pipeline_tag="text-to-speech",
    license="apache-2.0",
):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.config = config

        self.backbone, backbone_dim = _prepare_transformer(FLAVORS[config.backbone_flavor]())
        self.decoder, decoder_dim = _prepare_transformer(FLAVORS[config.decoder_flavor]())

        self.text_embeddings = nn.Embedding(config.text_vocab_size, backbone_dim)
        self.audio_embeddings = nn.Embedding(config.audio_vocab_size * config.audio_num_codebooks, backbone_dim)

        self.projection = nn.Linear(backbone_dim, decoder_dim, bias=False)
        self.codebook0_head = nn.Linear(backbone_dim, config.audio_vocab_size, bias=False)
        self.audio_head = nn.Parameter(torch.empty(config.audio_num_codebooks - 1, decoder_dim, config.audio_vocab_size))

    def setup_caches(self, max_batch_size: int) -> torch.Tensor:
        """Setup KV caches and return a causal mask."""
        dtype = next(self.parameters()).dtype
        device = next(self.parameters()).device

        with device:
            self.backbone.setup_caches(max_batch_size, dtype)
            self.decoder.setup_caches(max_batch_size, dtype, decoder_max_seq_len=self.config.audio_num_codebooks)

        self.register_buffer("backbone_causal_mask", _create_causal_mask(self.backbone.max_seq_len, device))
        self.register_buffer("decoder_causal_mask", _create_causal_mask(self.config.audio_num_codebooks, device))

    def generate_frame(
        self,
        tokens: torch.Tensor,
        tokens_mask: torch.Tensor,
        input_pos: torch.Tensor,
        temperature: float,
        topk: int,
    ) -> torch.Tensor:
        """
        Args:
            tokens: (batch_size, seq_len, audio_num_codebooks+1)
            tokens_mask: (batch_size, seq_len, audio_num_codebooks+1)
            input_pos: (batch_size, seq_len) positions for each token
            mask: (batch_size, seq_len, max_seq_len

        Returns:
            (batch_size, audio_num_codebooks) sampled tokens
        """
        dtype = next(self.parameters()).dtype
        b, s, _ = tokens.size()

        assert self.backbone.caches_are_enabled(), "backbone caches are not enabled"
        curr_backbone_mask = _index_causal_mask(self.backbone_causal_mask, input_pos)
        embeds = self._embed_tokens(tokens)
        masked_embeds = embeds * tokens_mask.unsqueeze(-1)
        h = masked_embeds.sum(dim=2)
        h = self.backbone(h, input_pos=input_pos, mask=curr_backbone_mask).to(dtype=dtype)

        last_h = h[:, -1, :]
        c0_logits = self.codebook0_head(last_h)
        c0_sample = sample_topk(c0_logits, topk, temperature)
        c0_embed = self._embed_audio(0, c0_sample)

        curr_h = torch.cat([last_h.unsqueeze(1), c0_embed], dim=1)
        curr_sample = c0_sample.clone()
        curr_pos = torch.arange(0, curr_h.size(1), device=curr_h.device).unsqueeze(0).repeat(curr_h.size(0), 1)

        # Decoder caches must be reset every frame.
        self.decoder.reset_caches()
        for i in range(1, self.config.audio_num_codebooks):
            curr_decoder_mask = _index_causal_mask(self.decoder_causal_mask, curr_pos)
            decoder_h = self.decoder(self.projection(curr_h), input_pos=curr_pos, mask=curr_decoder_mask).to(
                dtype=dtype
            )
            ci_logits = torch.mm(decoder_h[:, -1, :], self.audio_head[i - 1])
            ci_sample = sample_topk(ci_logits, topk, temperature)
            ci_embed = self._embed_audio(i, ci_sample)

            curr_h = ci_embed
            curr_sample = torch.cat([curr_sample, ci_sample], dim=1)
            curr_pos = curr_pos[:, -1:] + 1

        return curr_sample

    def reset_caches(self):
        self.backbone.reset_caches()
        self.decoder.reset_caches()

    def _embed_audio(self, codebook: int, tokens: torch.Tensor) -> torch.Tensor:
        return self.audio_embeddings(tokens + codebook * self.config.audio_vocab_size)

    def _embed_tokens(self, tokens: torch.Tensor) -> torch.Tensor:
        text_embeds = self.text_embeddings(tokens[:, :, -1]).unsqueeze(-2)

        audio_tokens = tokens[:, :, :-1] + (
            self.config.audio_vocab_size * torch.arange(self.config.audio_num_codebooks, device=tokens.device)
        )
        audio_embeds = self.audio_embeddings(audio_tokens.view(-1)).reshape(
            tokens.size(0), tokens.size(1), self.config.audio_num_codebooks, -1
        )

        return torch.cat([audio_embeds, text_embeds], dim=-2)


================================================
FILE: requirements.txt
================================================
torch==2.4.0
torchaudio==2.4.0
tokenizers==0.21.0
transformers==4.49.0
huggingface_hub==0.28.1
moshi==0.2.2
torchtune==0.4.0
torchao==0.9.0
silentcipher @ git+https://github.com/SesameAILabs/silentcipher@master

================================================
FILE: run_csm.py
================================================
import os
import torch
import torchaudio
from huggingface_hub import hf_hub_download
from generator import load_csm_1b, Segment
from dataclasses import dataclass

# Disable Triton compilation
os.environ["NO_TORCH_COMPILE"] = "1"

# Default prompts are available at https://hf.co/sesame/csm-1b
prompt_filepath_conversational_a = hf_hub_download(
    repo_id="sesame/csm-1b",
    filename="prompts/conversational_a.wav"
)
prompt_filepath_conversational_b = hf_hub_download(
    repo_id="sesame/csm-1b",
    filename="prompts/conversational_b.wav"
)

SPEAKER_PROMPTS = {
    "conversational_a": {
        "text": (
            "like revising for an exam I'd have to try and like keep up the momentum because I'd "
            "start really early I'd be like okay I'm gonna start revising now and then like "
            "you're revising for ages and then I just like start losing steam I didn't do that "
            "for the exam we had recently to be fair that was a more of a last minute scenario "
            "but like yeah I'm trying to like yeah I noticed this yesterday that like Mondays I "
            "sort of start the day with this not like a panic but like a"
        ),
        "audio": prompt_filepath_conversational_a
    },
    "conversational_b": {
        "text": (
            "like a super Mario level. Like it's very like high detail. And like, once you get "
            "into the park, it just like, everything looks like a computer game and they have all "
            "these, like, you know, if, if there's like a, you know, like in a Mario game, they "
            "will have like a question block. And if you like, you know, punch it, a coin will "
            "come out. So like everyone, when they come into the park, they get like this little "
            "bracelet and then you can go punching question blocks around."
        ),
        "audio": prompt_filepath_conversational_b
    }
}

def load_prompt_audio(audio_path: str, target_sample_rate: int) -> torch.Tensor:
    audio_tensor, sample_rate = torchaudio.load(audio_path)
    audio_tensor = audio_tensor.squeeze(0)
    # Resample is lazy so we can always call it
    audio_tensor = torchaudio.functional.resample(
        audio_tensor, orig_freq=sample_rate, new_freq=target_sample_rate
    )
    return audio_tensor

def prepare_prompt(text: str, speaker: int, audio_path: str, sample_rate: int) -> Segment:
    audio_tensor = load_prompt_audio(audio_path, sample_rate)
    return Segment(text=text, speaker=speaker, audio=audio_tensor)

def main():
    # Select the best available device, skipping MPS due to float64 limitations
    if torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"
    print(f"Using device: {device}")

    # Load model
    generator = load_csm_1b(device)

    # Prepare prompts
    prompt_a = prepare_prompt(
        SPEAKER_PROMPTS["conversational_a"]["text"],
        0,
        SPEAKER_PROMPTS["conversational_a"]["audio"],
        generator.sample_rate
    )

    prompt_b = prepare_prompt(
        SPEAKER_PROMPTS["conversational_b"]["text"],
        1,
        SPEAKER_PROMPTS["conversational_b"]["audio"],
        generator.sample_rate
    )

    # Generate conversation
    conversation = [
        {"text": "Hey how are you doing?", "speaker_id": 0},
        {"text": "Pretty good, pretty good. How about you?", "speaker_id": 1},
        {"text": "I'm great! So happy to be speaking with you today.", "speaker_id": 0},
        {"text": "Me too! This is some cool stuff, isn't it?", "speaker_id": 1}
    ]

    # Generate each utterance
    generated_segments = []
    prompt_segments = [prompt_a, prompt_b]

    for utterance in conversation:
        print(f"Generating: {utterance['text']}")
        audio_tensor = generator.generate(
            text=utterance['text'],
            speaker=utterance['speaker_id'],
            context=prompt_segments + generated_segments,
            max_audio_length_ms=10_000,
        )
        generated_segments.append(Segment(text=utterance['text'], speaker=utterance['speaker_id'], audio=audio_tensor))

    # Concatenate all generations
    all_audio = torch.cat([seg.audio for seg in generated_segments], dim=0)
    torchaudio.save(
        "full_conversation.wav",
        all_audio.unsqueeze(0).cpu(),
        generator.sample_rate
    )
    print("Successfully generated full_conversation.wav")

if __name__ == "__main__":
    main() 

================================================
FILE: setup.py
================================================
from setuptools import setup, find_packages
import os

# Read requirements from requirements.txt
with open('requirements.txt') as f:
    requirements = [line.strip() for line in f if line.strip() and not line.startswith('#')]

setup(
    name='csm',
    version='0.1.0',
    packages=find_packages(),
    install_requires=requirements,
)


================================================
FILE: watermarking.py
================================================
import argparse

import silentcipher
import torch
import torchaudio

# This watermark key is public, it is not secure.
# If using CSM 1B in another application, use a new private key and keep it secret.
CSM_1B_GH_WATERMARK = [212, 211, 146, 56, 201]


def cli_check_audio() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--audio_path", type=str, required=True)
    args = parser.parse_args()

    check_audio_from_file(args.audio_path)


def load_watermarker(device: str = "cuda") -> silentcipher.server.Model:
    model = silentcipher.get_model(
        model_type="44.1k",
        device=device,
    )
    return model


@torch.inference_mode()
def watermark(
    watermarker: silentcipher.server.Model,
    audio_array: torch.Tensor,
    sample_rate: int,
    watermark_key: list[int],
) -> tuple[torch.Tensor, int]:
    audio_array_44khz = torchaudio.functional.resample(audio_array, orig_freq=sample_rate, new_freq=44100)
    encoded, _ = watermarker.encode_wav(audio_array_44khz, 44100, watermark_key, calc_sdr=False, message_sdr=36)

    output_sample_rate = min(44100, sample_rate)
    encoded = torchaudio.functional.resample(encoded, orig_freq=44100, new_freq=output_sample_rate)
    return encoded, output_sample_rate


@torch.inference_mode()
def verify(
    watermarker: silentcipher.server.Model,
    watermarked_audio: torch.Tensor,
    sample_rate: int,
    watermark_key: list[int],
) -> bool:
    watermarked_audio_44khz = torchaudio.functional.resample(watermarked_audio, orig_freq=sample_rate, new_freq=44100)
    result = watermarker.decode_wav(watermarked_audio_44khz, 44100, phase_shift_decoding=True)

    is_watermarked = result["status"]
    if is_watermarked:
        is_csm_watermarked = result["messages"][0] == watermark_key
    else:
        is_csm_watermarked = False

    return is_watermarked and is_csm_watermarked


def check_audio_from_file(audio_path: str) -> None:
    watermarker = load_watermarker(device="cuda")

    audio_array, sample_rate = load_audio(audio_path)
    is_watermarked = verify(watermarker, audio_array, sample_rate, CSM_1B_GH_WATERMARK)

    outcome = "Watermarked" if is_watermarked else "Not watermarked"
    print(f"{outcome}: {audio_path}")


def load_audio(audio_path: str) -> tuple[torch.Tensor, int]:
    audio_array, sample_rate = torchaudio.load(audio_path)
    audio_array = audio_array.mean(dim=0)
    return audio_array, int(sample_rate)


if __name__ == "__main__":
    cli_check_audio()