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Configuration error
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# /// script
# requires-python = ">=3.12"
# dependencies = [
# "huggingface_hub",
# "moshi_mlx==0.2.12",
# "numpy",
# "sentencepiece",
# "sounddevice",
# "sphn",
# ]
# ///
import argparse
import json
import mlx.core as mx
import mlx.nn as nn
import sentencepiece
import sphn
from huggingface_hub import hf_hub_download
from moshi_mlx import models, utils
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("in_file", help="The file to transcribe.")
parser.add_argument("--max-steps", default=4096)
parser.add_argument("--hf-repo")
parser.add_argument(
"--vad", action="store_true", help="Enable VAD (Voice Activity Detection)."
)
args = parser.parse_args()
audio, _ = sphn.read(args.in_file, sample_rate=24000)
if args.hf_repo is None:
if args.vad:
args.hf_repo = "kyutai/stt-1b-en_fr-candle"
else:
args.hf_repo = "kyutai/stt-1b-en_fr-mlx"
lm_config = hf_hub_download(args.hf_repo, "config.json")
with open(lm_config, "r") as fobj:
lm_config = json.load(fobj)
mimi_weights = hf_hub_download(args.hf_repo, lm_config["mimi_name"])
moshi_name = lm_config.get("moshi_name", "model.safetensors")
moshi_weights = hf_hub_download(args.hf_repo, moshi_name)
text_tokenizer = hf_hub_download(args.hf_repo, lm_config["tokenizer_name"])
lm_config = models.LmConfig.from_config_dict(lm_config)
model = models.Lm(lm_config)
model.set_dtype(mx.bfloat16)
if moshi_weights.endswith(".q4.safetensors"):
nn.quantize(model, bits=4, group_size=32)
elif moshi_weights.endswith(".q8.safetensors"):
nn.quantize(model, bits=8, group_size=64)
print(f"loading model weights from {moshi_weights}")
if args.hf_repo.endswith("-candle"):
model.load_pytorch_weights(moshi_weights, lm_config, strict=True)
else:
model.load_weights(moshi_weights, strict=True)
print(f"loading the text tokenizer from {text_tokenizer}")
text_tokenizer = sentencepiece.SentencePieceProcessor(text_tokenizer) # type: ignore
print(f"loading the audio tokenizer {mimi_weights}")
audio_tokenizer = models.mimi.Mimi(models.mimi_202407(32))
audio_tokenizer.load_pytorch_weights(str(mimi_weights), strict=True)
print("warming up the model")
model.warmup()
gen = models.LmGen(
model=model,
max_steps=args.max_steps,
text_sampler=utils.Sampler(top_k=25, temp=0),
audio_sampler=utils.Sampler(top_k=250, temp=0.8),
check=False,
)
print(f"starting inference {audio.shape}")
audio = mx.concat([mx.array(audio), mx.zeros((1, 48000))], axis=-1)
last_print_was_vad = False
for start_idx in range(0, audio.shape[-1] // 1920 * 1920, 1920):
block = audio[:, None, start_idx : start_idx + 1920]
other_audio_tokens = audio_tokenizer.encode_step(block).transpose(0, 2, 1)
if args.vad:
text_token, vad_heads = gen.step_with_extra_heads(other_audio_tokens[0])
if vad_heads:
pr_vad = vad_heads[2][0, 0, 0].item()
if pr_vad > 0.5 and not last_print_was_vad:
print(" [end of turn detected]")
last_print_was_vad = True
else:
text_token = gen.step(other_audio_tokens[0])
text_token = text_token[0].item()
audio_tokens = gen.last_audio_tokens()
_text = None
if text_token not in (0, 3):
_text = text_tokenizer.id_to_piece(text_token) # type: ignore
_text = _text.replace("▁", " ")
print(_text, end="", flush=True)
last_print_was_vad = False
print()
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