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2025/03/13 - We are releasing the 1B CSM variant. Code is available on GitHub: SesameAILabs/csm.
CSM (Conversational Speech Model) is a speech generation model from Sesame [blocked] that generates RVQ audio codes from text and audio inputs. The model architecture employs a Llama backbone and a smaller audio decoder that produces Mimi audio codes.
A fine-tuned variant of CSM powers the interactive voice demo shown in our blog post.
A hosted HuggingFace space is also available for testing audio generation.
Setup the repo
Generate a sentence
CSM sounds best when provided with context. You can prompt or provide context to the model using a Segment for each speaker utterance.
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.
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:
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.
git clone git@github.com:SesameAILabs/csm.gitcd csmpython3.10 -m venv .venvsource .venv/bin/activatepip install -r requirements.txt
# You will need access to sesame/csm-1b and meta-llama/Llama-3.2-1Bhuggingface-cli loginfrom generator import load_csm_1bimport torchaudio
generator = load_csm_1b(device="cuda")
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)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)