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feat: add multi-channel support and language filtering to focus on specific speakers

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Adolfo Reyna
2026-03-01 11:39:03 -05:00
parent 00231cc747
commit 8f6d2a0303
2 changed files with 25 additions and 3 deletions
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README.md
+4
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@@ -65,6 +65,10 @@ Run the script using `python3 transcribe.py` with optional flags.
- `-c`, `--context`: Enable prompt caching/rolling context to help the model maintain sentence continuity across chunks.
- `--lang [CODE]`: Hardcode the source language (e.g., `en`, `es`) to bypass automatic language detection for faster processing.
- `--silence [MS]`: Set the minimum silence duration in milliseconds to end a chunk. Defaults to 1000ms. Increase to force longer sentences before translation.
- `--max-buffer [SEC]`: Maximum buffer duration in seconds before forcing a flush (default: 20).
- `--channels [N]`: Number of input channels (default: 1).
- `--pick-channel [0|1]`: If stereo, select channel 0 (Left) or 1 (Right) to focus transcription.
- `--filter-lang`: If used with `--lang`, discards segments that do not match the target language.
---
transcribe.py
+21 -3
View File
@@ -43,7 +43,6 @@ TARGET_LANGS = {
"ar": "Helsinki-NLP/opus-mt-en-ar" # Added Arabic as discussed before
}
CHANNELS = 1
SAMPLERATE = 16000
BLOCK_SIZE = 512
VAD_THRESHOLD = 0.5
@@ -127,6 +126,9 @@ def main():
parser.add_argument("--lang", type=str, help="Hardcode source language (e.g. 'en', 'es') to bypass detection")
parser.add_argument("--silence", type=int, default=1000, help="Minimum silence duration in ms to end a chunk (default: 1000)")
parser.add_argument("--max-buffer", type=int, default=20, help="Maximum buffer duration in seconds before forcing a flush (default: 20)")
parser.add_argument("--channels", type=int, default=1, help="Number of input channels (default: 1)")
parser.add_argument("--pick-channel", type=int, choices=[0, 1], help="Pick a specific channel (0 or 1) from stereo input")
parser.add_argument("--filter-lang", action="store_true", help="Discard segments where detected language does not match --lang")
args = parser.parse_args()
@@ -210,10 +212,17 @@ def main():
last_draft_text = ""
try:
with sd.InputStream(samplerate=SAMPLERATE, channels=CHANNELS, callback=callback, blocksize=BLOCK_SIZE, device=device_index):
with sd.InputStream(samplerate=SAMPLERATE, channels=args.channels, callback=callback, blocksize=BLOCK_SIZE, device=device_index):
while True:
while not audio_queue.empty():
data = audio_queue.get()
if args.channels > 1:
if args.pick_channel is not None:
# Select specific channel
data = data[:, args.pick_channel]
else:
# Mix to mono
data = np.mean(data, axis=1)
audio_buffer.append(data.flatten())
if len(audio_buffer) > 0:
@@ -269,6 +278,11 @@ def main():
original_text = transcription_result['text'].strip()
detected_lang = transcription_result.get('language', args.lang if args.lang else 'en')
# Filter language if requested
if args.filter_lang and args.lang and detected_lang != args.lang:
print(f"\n[SYSTEM]: Discarding segment (Detected: {detected_lang}, Expected: {args.lang})")
original_text = ""
if is_hallucination(original_text):
print(f"\n[SYSTEM]: Hallucination detected, ignoring and resetting context.")
original_text = ""
@@ -344,7 +358,11 @@ def main():
draft_result = mlx_whisper.transcribe(current_audio, **draft_kwargs)
draft_text = draft_result['text'].strip()
draft_lang = draft_result.get('language', args.lang if args.lang else 'en')
if args.filter_lang and args.lang and draft_lang != args.lang:
draft_text = ""
if draft_text:
# Update timestamp ONLY if the text actually changed
if draft_text != last_draft_text: