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