https://github.com/SesameAILabs/csm
A Conversational Speech Generation Model
https://github.com/SesameAILabs/csm
Last synced: about 2 months ago
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A Conversational Speech Generation Model
- Host: GitHub
- URL: https://github.com/SesameAILabs/csm
- Owner: SesameAILabs
- License: apache-2.0
- Created: 2025-02-26T15:38:54.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-13T20:23:50.000Z (about 2 months ago)
- Last Synced: 2025-03-13T20:32:48.389Z (about 2 months ago)
- Size: 6.84 KB
- Stars: 6,867
- Watchers: 1,108
- Forks: 226
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CSM
**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 [email protected]:SesameAILabs/csm.git
cd csm
python3.10 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
# 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`.
## Usage
Generate a sentence
```python
from huggingface_hub import hf_hub_download
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"
model_path = hf_hub_download(repo_id="sesame/csm-1b", filename="ckpt.pt")
generator = load_csm_1b(model_path, 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)
```
CSM sounds best when provided with context. You can prompt or provide context to the model using a `Segment` for each speaker's utterance.
```python
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.