119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
import sys
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from unittest.mock import MagicMock
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# Comprehensive workaround for missing _lzma in some Python builds
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try:
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import lzma
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except ImportError:
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mock_lzma = MagicMock()
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# Add common constants that libraries expect from lzma
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mock_lzma.FORMAT_XZ = 1
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mock_lzma.FORMAT_ALONE = 2
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mock_lzma.FORMAT_RAW = 3
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mock_lzma.CHECK_NONE = 0
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mock_lzma.CHECK_CRC32 = 1
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mock_lzma.CHECK_CRC64 = 4
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mock_lzma.CHECK_SHA256 = 10
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sys.modules["_lzma"] = MagicMock()
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sys.modules["lzma"] = mock_lzma
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import mlx_whisper
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import numpy as np
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import sounddevice as sd
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import queue
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import torch
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from silero_vad import load_silero_vad, get_speech_timestamps
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from transformers import MarianMTModel, MarianTokenizer
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# Parameters
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WHISPER_MODEL = "mlx-community/whisper-small.en-mlx"
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TRANSLATE_MODEL = "Helsinki-NLP/opus-mt-en-es"
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CHANNELS = 1
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SAMPLERATE = 16000
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BLOCK_SIZE = 512
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VAD_THRESHOLD = 0.5
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BUFFER_LIMIT = SAMPLERATE * 30
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MIN_SILENCE_DURATION_MS = 500
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audio_queue = queue.Queue()
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def callback(indata, frames, time, status):
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if status:
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print(status, file=sys.stderr)
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audio_queue.put(indata.copy())
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def main():
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# Set device for translation model
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device = "mps" if torch.backends.mps.is_available() else "cpu"
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print(f"Using device for translation: {device}")
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print(f"Loading Whisper model '{WHISPER_MODEL}'...")
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print(f"Loading dedicated translation model '{TRANSLATE_MODEL}'...")
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tokenizer = MarianTokenizer.from_pretrained(TRANSLATE_MODEL)
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model = MarianMTModel.from_pretrained(TRANSLATE_MODEL).to(device)
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print("Loading Silero VAD model...")
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vad_model = load_silero_vad()
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print("Models loaded.")
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print(f"\nStarting live transcription & translation... (Press Ctrl+C to stop)")
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audio_buffer = []
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speech_started = False
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try:
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with sd.InputStream(samplerate=SAMPLERATE, channels=CHANNELS, callback=callback, blocksize=BLOCK_SIZE):
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while True:
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while not audio_queue.empty():
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data = audio_queue.get()
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audio_buffer.append(data.flatten())
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if len(audio_buffer) > 0:
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current_audio = np.concatenate(audio_buffer)
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audio_tensor = torch.from_numpy(current_audio)
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speech_timestamps = get_speech_timestamps(
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audio_tensor,
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vad_model,
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sampling_rate=SAMPLERATE,
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threshold=VAD_THRESHOLD,
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min_silence_duration_ms=MIN_SILENCE_DURATION_MS
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)
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if len(speech_timestamps) > 0:
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speech_started = True
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last_end = speech_timestamps[-1]['end']
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buffer_len_samples = len(current_audio)
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if (buffer_len_samples - last_end) > (SAMPLERATE * MIN_SILENCE_DURATION_MS / 1000) or buffer_len_samples > BUFFER_LIMIT:
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# 1. Transcribe (English)
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result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=WHISPER_MODEL)
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original_text = result['text'].strip()
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if original_text:
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# 2. Translate
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inputs = tokenizer(original_text, return_tensors="pt", padding=True).to(device)
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with torch.no_grad():
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translated_tokens = model.generate(**inputs)
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translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
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print(f"\nEN: {original_text}")
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print(f"ES: {translated_text}")
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audio_buffer = []
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speech_started = False
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elif not speech_started and len(current_audio) > SAMPLERATE * 2:
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audio_buffer = []
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except KeyboardInterrupt:
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print("\nStopped by user.")
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except Exception as e:
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print(f"\nError: {e}")
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if __name__ == "__main__":
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main()
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