Files
whisper-translation/transcribe.py
2026-02-26 20:59:57 -05:00

85 lines
2.9 KiB
Python

import mlx_whisper
import numpy as np
import sounddevice as sd
import queue
import sys
import torch
from silero_vad import load_silero_vad, get_speech_timestamps
# Parameters
MODEL_PATH = "mlx-community/whisper-small.en-mlx" # MLX optimized small model
CHANNELS = 1
SAMPLERATE = 16000
BLOCK_SIZE = 512 # Silero VAD prefers 512, 1024, or 1536
VAD_THRESHOLD = 0.5
BUFFER_LIMIT = SAMPLERATE * 30
MIN_SILENCE_DURATION_MS = 500
audio_queue = queue.Queue()
def callback(indata, frames, time, status):
if status:
print(status, file=sys.stderr)
audio_queue.put(indata.copy())
def main():
print(f"Loading MLX-optimized Whisper model '{MODEL_PATH}'...")
# mlx-whisper uses the same model names or Hugging Face paths
print("Loading Silero VAD model...")
vad_model = load_silero_vad()
print("Models loaded.")
print("\nStarting live transcription (MLX + VAD)... (Press Ctrl+C to stop)")
audio_buffer = []
speech_started = False
try:
with sd.InputStream(samplerate=SAMPLERATE, channels=CHANNELS, callback=callback, blocksize=BLOCK_SIZE):
while True:
while not audio_queue.empty():
data = audio_queue.get()
audio_buffer.append(data.flatten())
if len(audio_buffer) > 0:
current_audio = np.concatenate(audio_buffer)
audio_tensor = torch.from_numpy(current_audio)
speech_timestamps = get_speech_timestamps(
audio_tensor,
vad_model,
sampling_rate=SAMPLERATE,
threshold=VAD_THRESHOLD,
min_silence_duration_ms=MIN_SILENCE_DURATION_MS
)
if len(speech_timestamps) > 0:
speech_started = True
last_end = speech_timestamps[-1]['end']
buffer_len_samples = len(current_audio)
if (buffer_len_samples - last_end) > (SAMPLERATE * MIN_SILENCE_DURATION_MS / 1000) or buffer_len_samples > BUFFER_LIMIT:
# Transcribe with MLX
result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=MODEL_PATH)
text = result['text'].strip()
if text:
print(f"Transcription: {text}")
audio_buffer = []
speech_started = False
elif not speech_started and len(current_audio) > SAMPLERATE * 2:
audio_buffer = []
except KeyboardInterrupt:
print("\nStopped by user.")
except Exception as e:
print(f"\nError: {e}")
if __name__ == "__main__":
main()