llm-jp
/

Llama-Mimi-8B

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 ---
 library_name: transformers
+license: llama3.1
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B
+pipeline_tag: audio-to-audio
 ---
+# Llama-Mimi-8B
+[📃Paper](https://arxiv.org/abs/2509.14882) | [🧑‍💻Code](https://github.com/llm-jp/llama-mimi) | [🗣️Demo](https://speed1313.github.io/llama-mimi/)
+<img src="https://speed1313.github.io/llama-mimi/data/llama-mimi.svg" width="50%"/>
+## Introduction
+Llama-Mimi is a speech language model that uses a unified tokenizer (Mimi) and a single Transformer decoder (Llama) to jointly model sequences of interleaved semantic and acoustic tokens.
+Trained on ~240k hours of English audio, Llama-Mimi achieves state-of-the-art performance in acoustic consistency on [SALMon](https://arxiv.org/abs/2409.07437) and effectively preserves speaker identity.
+Visit our [demo site](https://speed1313.github.io/llama-mimi/) to hear generated speech samples.
+## Models
+- Llama-Mimi-1.3B, https://huggingface.co/llm-jp/Llama-Mimi-1.3b
+- Llama-Mimi-8B, https://huggingface.co/llm-jp/Llama-Mimi-8b
+## How to Use
+Generate audio continuations from a given audio prompt.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from transformers import MimiModel, AutoFeatureExtractor
+from transformers import StoppingCriteria
+import random
+import numpy as np
+import torchaudio
+import soundfile as sf
+import re
+def text_to_audio_values(
+    text: str,
+    num_quantizers: int,
+    output_file: str,
+    audio_tokenizer,
+    feature_extractor,
+):
+    # Extract (val, idx) pairs from the <val_idx> format in the text
+    matches = re.findall(r"<(\d+)_(\d+)>", text)
+    vals = []
+    for i in range(0, len(matches), num_quantizers):
+        chunk = matches[i : i + num_quantizers]
+        if len(chunk) < num_quantizers:
+            break
+        indices = [int(idx) for _, idx in chunk]
+        if indices == list(range(num_quantizers)):
+            vals.extend(int(val) for val, _ in chunk)
+        else:
+            break
+    vals = vals[: len(vals) - len(vals) % num_quantizers]
+    tensor_bt4 = torch.tensor(vals).reshape(1, -1, num_quantizers)  # (B, T, 4)
+    tensor_b4t = tensor_bt4.transpose(1, 2)  # (B, 4, T)
+    audio_values = audio_tokenizer.decode(tensor_b4t)[0]
+    sf.write(
+        output_file,
+        audio_values[0][0].detach().cpu().numpy(),
+        feature_extractor.sampling_rate,
+    )
+def audio_array_to_text(
+    audio_array: torch.tensor,
+    audio_tokenizer,
+    feature_extractor,
+    num_quantizers: int,
+    max_seconds: int = 20,
+) -> str:
+    # truncate the audio array to the expected length
+    if audio_array.shape[-1] > max_seconds * feature_extractor.sampling_rate:
+        audio_array = audio_array[: max_seconds * feature_extractor.sampling_rate]
+        #
+    inputs = feature_extractor(
+        raw_audio=audio_array,
+        sampling_rate=feature_extractor.sampling_rate,
+        return_tensors="pt",
+    ).to(audio_tokenizer.device)
+    with torch.no_grad():
+        encoder_outputs = audio_tokenizer.encode(
+            inputs["input_values"],
+            inputs["padding_mask"],
+            num_quantizers=num_quantizers,
+        )
+    flatten_audio_codes = encoder_outputs.audio_codes.transpose(1, 2).reshape(-1)
+    assert flatten_audio_codes.numel() % num_quantizers == 0
+    steps = []
+    for i in range(0, flatten_audio_codes.numel(), num_quantizers):
+        group = [
+            f"<{flatten_audio_codes[i + j].item()}_{j}>"
+            for j in range(num_quantizers)
+        ]
+        steps.append(group)
+    parts = [tok for step in steps for tok in step]
+    text = "".join(parts)
+    del inputs, encoder_outputs, flatten_audio_codes
+    torch.cuda.empty_cache()
+    return f"<audio>{text}</audio>"
+def set_determinism(seed: int = 42) -> None:
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+class StopOnAudioEnd(StoppingCriteria):
+    def __init__(self, tokenizer):
+        self.tokenizer = tokenizer
+        self.target_text = "</audio>"
+        self.target_ids = tokenizer(
+            self.target_text, add_special_tokens=False
+        ).input_ids
+    def __call__(self, input_ids, scores, **kwargs):
+        if len(input_ids[0]) < len(self.target_ids):
+            return False
+        return input_ids[0][-len(self.target_ids) :].tolist() == self.target_ids
+set_determinism()
+temperature = 0.8
+top_k = 30
+do_sample = True
+max_length = 1024
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = "llm-jp/Llama-Mimi-8B"
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16).eval().to(device)
+num_quantizers = model.config.num_quantizers
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+audio_tokenizer = MimiModel.from_pretrained("kyutai/mimi")
+feature_extractor = AutoFeatureExtractor.from_pretrained("kyutai/mimi")
+stopping_criteria = StopOnAudioEnd(tokenizer)
+audio_file = "assets/great_day_gt.wav"
+waveform, sample_rate = torchaudio.load(audio_file)
+if sample_rate != feature_extractor.sampling_rate:
+    waveform = torchaudio.transforms.Resample(sample_rate, feature_extractor.sampling_rate)(waveform)
+    sample_rate = feature_extractor.sampling_rate
+prompt_array = waveform.squeeze().cpu().numpy()
+text = audio_array_to_text(
+    prompt_array, audio_tokenizer, feature_extractor, num_quantizers
+)
+text = text.replace("</audio>", "")
+inputs = tokenizer(text, return_tensors="pt").to(device)
+with torch.no_grad():
+    generated = model.generate(
+        **inputs,
+        max_length=max_length,
+        do_sample=do_sample,
+        temperature=temperature,
+        top_k=top_k,
+        stopping_criteria=[stopping_criteria],
+    )
+generated_text = tokenizer.decode(generated[0])
+text_to_audio_values(
+    generated_text,
+    num_quantizers=num_quantizers,
+    output_file="output.wav",
+    audio_tokenizer=audio_tokenizer,
+    feature_extractor=feature_extractor,
+)
+```
+## Citation
+```
+@misc{sugiura2025llamamimispeechlanguagemodels,
+      title={Llama-Mimi: Speech Language Models with Interleaved Semantic and Acoustic Tokens},
+      author={Issa Sugiura and Shuhei Kurita and Yusuke Oda and Ryuichiro Higashinaka},
+      year={2025},
+      eprint={2509.14882},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2509.14882},
+}
+```