Csm 1b

🚀 CSM 1B

CSM (Conversational Speech Model) is a speech generation model from Sesame. It can generate RVQ audio codes from text and audio inputs. The model uses a Llama backbone and a smaller audio decoder to produce Mimi audio codes. A fine - tuned variant powers the interactive voice demo in our blog post. There's also a hosted HuggingFace space for audio generation testing.

🚀 Quick Start

• 2025/03/13: We released the 1B CSM variant. The original code is available on GitHub: SesameAILabs/csm.
• 2025/05/07: CSM is supported in Transformers.

💻 Usage Examples

Basic Usage

Generate a sentence

import torch
from transformers import CsmForConditionalGeneration, AutoProcessor

model_id = "eustlb/csm-1b"
device = "cuda" if torch.cuda.is_available() else "cpu"

# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)

# prepare the inputs
text = "[0]The past is just a story we tell ourselves." # `[0]` for speaker id 0
inputs = processor(text, add_special_tokens=True).to(device)

# another equivalent way to prepare the inputs
conversation = [
    {"role": "0", "content": [{"type": "text", "text": "The past is just a story we tell ourselves."}]},
]
inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    return_dict=True,
).to(device)

# infer the model
audio = model.generate(**inputs, output_audio=True)
processor.save_audio(audio, "example_without_context.wav")

Advanced Usage

Generate with context

import torch
from transformers import CsmForConditionalGeneration, AutoProcessor
from datasets import load_dataset, Audio

model_id = "eustlb/csm-1b"
device = "cuda" if torch.cuda.is_available() else "cpu"

# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)

# prepare the inputs
ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
# ensure the audio is 24kHz
ds = ds.cast_column("audio", Audio(sampling_rate=24000))
conversation = []

# 1. context
for text, audio, speaker_id in zip(ds[:4]["text"], ds[:4]["audio"], ds[:4]["speaker_id"]):
    conversation.append(
        {
            "role": f"{speaker_id}",
            "content": [{"type": "text", "text": text}, {"type": "audio", "path": audio["array"]}],
        }
    )

# 2. text prompt
conversation.append({"role": f"{ds[4]['speaker_id']}", "content": [{"type": "text", "text": ds[4]["text"]}]})

inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    return_dict=True,
).to(device)

# infer the model
audio = model.generate(**inputs, output_audio=True)
processor.save_audio(audio, "example_with_context.wav")

Batched Inference

import torch
from transformers import CsmForConditionalGeneration, AutoProcessor
from datasets import load_dataset, Audio

model_id = "eustlb/csm-1b"
device = "cuda" if torch.cuda.is_available() else "cpu"

# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)

# prepare the inputs 
ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
# ensure the audio is 24kHz
ds = ds.cast_column("audio", Audio(sampling_rate=24000))
# here a batch with two prompts
conversation = [
    [
        {
            "role": f"{ds[0]['speaker_id']}",
            "content": [
                {"type": "text", "text": ds[0]["text"]},
                {"type": "audio", "path": ds[0]["audio"]["array"]},
            ],
        },
        {
            "role": f"{ds[1]['speaker_id']}",
            "content": [
                {"type": "text", "text": ds[1]["text"]},
            ],
        },
    ],
    [
        {
            "role": f"{ds[0]['speaker_id']}",
            "content": [
                {"type": "text", "text": ds[0]["text"]},
            ],
        }
    ],
]
inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    return_dict=True,
).to(device)

audio = model.generate(**inputs, output_audio=True)
processor.save_audio(audio, [f"speech_batch_idx_{i}.wav" for i in range(len(audio))])

Making The Model Go Brrr

import torch
import copy
from transformers import CsmForConditionalGeneration, AutoProcessor
from datasets import load_dataset

model_id = "eustlb/csm-1b"
device = "cuda"

# set logs to ensure no recompilation and graph breaks
torch._logging.set_logs(graph_breaks=True, recompiles=True, cudagraphs=True)

# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)

# use static cache, enabling automatically torch compile with fullgraph and reduce-overhead
model.generation_config.max_length = 250 # big enough to avoid recompilation
model.generation_config.max_new_tokens = None # would take precedence over max_length
model.generation_config.cache_implementation = "static"
model.depth_decoder.generation_config.cache_implementation = "static"

# generation kwargs
gen_kwargs = {
    "do_sample": False,
    "depth_decoder_do_sample": False,
    "temperature": 1.0,
    "depth_decoder_temperature": 1.0,
}

# Define a timing decorator
class TimerContext:
    def __init__(self, name="Execution"):
        self.name = name
        self.start_event = None
        self.end_event = None
        
    def __enter__(self):
        # Use CUDA events for more accurate GPU timing
        self.start_event = torch.cuda.Event(enable_timing=True)
        self.end_event = torch.cuda.Event(enable_timing=True)
        self.start_event.record()
        return self

    def __exit__(self, *args):
        self.end_event.record()
        torch.cuda.synchronize()
        elapsed_time = self.start_event.elapsed_time(self.end_event) / 1000.0
        print(f"{self.name} time: {elapsed_time:.4f} seconds")

# prepare the inputs 
ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")

conversation = [
    {
        "role": f"{ds[0]['speaker_id']}",
        "content": [
            {"type": "text", "text": ds[0]["text"]},
            {"type": "audio", "path": ds[0]["audio"]["array"]},
        ],
    },
    {
        "role": f"{ds[1]['speaker_id']}",
        "content": [
            {"type": "text", "text": ds[1]["text"]},
            {"type": "audio", "path": ds[1]["audio"]["array"]},
        ],
    },
    {
        "role": f"{ds[2]['speaker_id']}",
        "content": [
            {"type": "text", "text": ds[2]["text"]},
        ],
    },
]

padded_inputs_1 = processor.apply_chat_template(
    conversation,
    tokenize=True,
    return_dict=True,
).to(device)

print("\n" + "="*50)
print("First generation - compiling and recording CUDA graphs...")
with TimerContext("First generation"):
    _ = model.generate(**padded_inputs_1, **gen_kwargs)
print("="*50)

print("\n" + "="*50)
print("Second generation - fast !!!")
with TimerContext("Second generation"):
    _ = model.generate(**padded_inputs_1, **gen_kwargs)
print("="*50)

# now with different inputs
conversation = [
    {
        "role": f"{ds[0]['speaker_id']}",
        "content": [
            {"type": "text", "text": ds[2]["text"]},
            {"type": "audio", "path": ds[2]["audio"]["array"]},
        ],
    },
    {
        "role": f"{ds[1]['speaker_id']}",
        "content": [
            {"type": "text", "text": ds[3]["text"]},
            {"type": "audio", "path": ds[3]["audio"]["array"]},
        ],
    },
    {
        "role": f"{ds[2]['speaker_id']}",
        "content": [
            {"type": "text", "text": ds[4]["text"]},
        ],
    },
]
padded_inputs_2 = processor.apply_chat_template(
    conversation,
    tokenize=True,
    return_dict=True,
).to(device)

print("\n" + "="*50)
print("Generation with other inputs!")
with TimerContext("Generation with different inputs"):
    _ = model.generate(**padded_inputs_2, **gen_kwargs)
print("="*50)

Fine - tuning & training

from datasets import load_dataset, Audio
from transformers import (
    CsmForConditionalGeneration,
    TrainingArguments,
    CsmProcessor,
    Trainer
)

processor = CsmProcessor.from_pretrained("eustlb/csm-1b")
model = CsmForConditionalGeneration.from_pretrained("eustlb/csm-1b")
model.train()

ds = load_dataset("eustlb/dailytalk-conversations-grouped", split="train")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))

def data_collator(samples):
    conversations = [] 

    for sample in samples:
        concatenated_audio_array = sample["audio"]["array"]
        audio = [concatenated_audio_array[s: e] for s, e in sample["audio_cut_idxs"]]
            
        conversation = []
        for speaker_id, text, audio in zip(sample["speaker_ids"], sample["texts"], audio):
            conversation.append({
                "role": f"{speaker_id}",
                "content": [
                    {"type": "text", "text": text},
                    {"type": "audio", "audio": audio}
                ]
            })
            
        conversations.append(conversation)

    inputs = processor.apply_chat_template(
        conversations,
        tokenize=True,
        return_dict=True,
        output_labels=True,
    )
    return inputs

training_args = TrainingArguments(
    "test-trainer",
    remove_unused_columns=False,
    gradient_checkpointing=True,
)

trainer = Trainer(
    model, 
    training_args,
    train_dataset=ds,
    data_collator=data_collator,
)

trainer.train()

📚 Documentation

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.

📄 License

This project is licensed under the Apache 2.0 license.