CSM 1B (Safetensors)
Converted from the
original version to various Safetensors formats. It also tracks downloads.
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 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.
Usage
Setup the repo
1python -m venv .venv
2source .venv/bin/activate
3curl -s -L https://raw.githubusercontent.com/SesameAILabs/csm/refs/heads/main/requirements.txt | pip install -r /dev/stdin
4
5# You will need access to sesame/csm-1b and meta-llama/Llama-3.2-1B
6huggingface-cli login
Generate a sentence
1from generator import load_csm_1b
2import torchaudio
3
4generator = load_csm_1b(device="cuda")
5
6audio = generator.generate(
7 text="Hello from Sesame.",
8 speaker=0,
9 context=[],
10 max_audio_length_ms=10_000,
11)
12
13torchaudio.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 utterance.
1speakers = [0, 1, 0, 0]
2transcripts = [
3 "Hey how are you doing.",
4 "Pretty good, pretty good.",
5 "I'm great.",
6 "So happy to be speaking to you.",
7]
8audio_paths = [
9 "utterance_0.wav",
10 "utterance_1.wav",
11 "utterance_2.wav",
12 "utterance_3.wav",
13]
14
15def load_audio(audio_path):
16 audio_tensor, sample_rate = torchaudio.load(audio_path)
17 audio_tensor = torchaudio.functional.resample(
18 audio_tensor.squeeze(0), orig_freq=sample_rate, new_freq=generator.sample_rate
19 )
20 return audio_tensor
21
22segments = [
23 Segment(text=transcript, speaker=speaker, audio=load_audio(audio_path))
24 for transcript, speaker, audio_path in zip(transcripts, speakers, audio_paths)
25]
26audio = generator.generate(
27 text="Me too, this is some cool stuff huh?",
28 speaker=1,
29 context=segments,
30 max_audio_length_ms=10_000,
31)
32
33torchaudio.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.
