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Support multilingual detection and bridge translation via Whisper small

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Adolfo Reyna
2026-02-26 21:43:50 -05:00
parent da73d4e0c7
commit ae2b54da9b
1 changed files with 32 additions and 20 deletions
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40
transcribe.py
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@@ -25,8 +25,9 @@ from silero_vad import load_silero_vad, get_speech_timestamps
from transformers import MarianMTModel, MarianTokenizer
# Parameters
WHISPER_MODEL = "mlx-community/whisper-small.en-mlx"
# List of language pairs (English to ...)
# Using the multilingual small model
WHISPER_MODEL = "mlx-community/whisper-small-mlx"
# Translation models (English -> Target)
TARGET_LANGS = {
"Spanish": "Helsinki-NLP/opus-mt-en-es",
"French": "Helsinki-NLP/opus-mt-en-fr",
@@ -51,8 +52,8 @@ def main():
device = "mps" if torch.backends.mps.is_available() else "cpu"
print(f"Using device: {device}")
# 1. Load models first
print(f"Loading Whisper model '{WHISPER_MODEL}'...")
# 1. Load models
print(f"Loading Multilingual Whisper model '{WHISPER_MODEL}'...")
translation_engines = {}
for lang_name, model_id in TARGET_LANGS.items():
@@ -76,13 +77,12 @@ def main():
print("Invalid input, using default device.")
device_index = None
print(f"\nStarting live transcription & multiple translations using device {device_index if device_index is not None else 'default'}... (Press Ctrl+C to stop)")
print(f"\nStarting Multilingual live transcription... (Press Ctrl+C to stop)")
audio_buffer = []
speech_started = False
try:
# 3. Start stream
with sd.InputStream(samplerate=SAMPLERATE, channels=CHANNELS, callback=callback, blocksize=BLOCK_SIZE, device=device_index):
while True:
while not audio_queue.empty():
@@ -108,19 +108,31 @@ def main():
if (buffer_len_samples - last_end) > (SAMPLERATE * MIN_SILENCE_DURATION_MS / 1000) or buffer_len_samples > BUFFER_LIMIT:
# Transcribe once
result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=WHISPER_MODEL)
original_text = result['text'].strip()
# 1. Transcribe & Detect Language
# We use task="transcribe" to get the original text and detect language
transcription_result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=WHISPER_MODEL)
original_text = transcription_result['text'].strip()
detected_lang = transcription_result.get('language', 'unknown')
if original_text:
if len(original_text) > 250:
original_text = original_text[:247] + "..."
print(f"\n[{detected_lang.upper()} detected]: {original_text}")
print(f"\n[EN]: {original_text}")
# 2. Bridge to English if not already English
if detected_lang != "en":
# Use Whisper to translate the segment to English
bridge_result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=WHISPER_MODEL, task="translate")
english_text = bridge_result['text'].strip()
print(f"[EN Bridge]: {english_text}")
else:
english_text = original_text
# 3. Translate from English to other languages
if english_text:
if len(english_text) > 250:
english_text = english_text[:247] + "..."
# Translate to all targets
for lang_name, (model, tokenizer) in translation_engines.items():
inputs = tokenizer(original_text, return_tensors="pt", padding=True).to(device)
inputs = tokenizer(english_text, return_tensors="pt", padding=True).to(device)
with torch.no_grad():
translated_tokens = model.generate(**inputs, max_new_tokens=150)
translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)