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feat: add hallucination detection and watchdog for stuck transcriptions

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This commit is contained in:
Adolfo Reyna
2026-03-01 11:15:59 -05:00
parent 2999502434
commit 00231cc747
2 changed files with 111 additions and 3 deletions
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README.md
+5
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@@ -34,6 +34,10 @@ Run the script using `python3 transcribe.py` with optional flags.
```bash
python3 transcribe.py -l
```
- **Caption system audio (speakers) using a loopback device:**
```bash
python3 transcribe.py --loopback -es
```
- **Transcribe and translate to Spanish (screen only):**
```bash
python3 transcribe.py -es
@@ -55,6 +59,7 @@ Run the script using `python3 transcribe.py` with optional flags.
- `-i`, `--ingest`: Enable data transmission to the remote server.
- `-l`, `--list-devices`: Show available audio devices and exit.
- `-d`, `--device [ID]`: Input device index (bypasses selection prompt).
- `--loopback`: Automatically select a loopback device (e.g., BlackHole, Stereo Mix) to caption system audio.
- `-q`, `--quantize`: Use 4-bit quantized Whisper model for faster transcription (Strategy 3).
- `-s`, `--stream`: Enable real-time streaming transcription/draft mode (Strategy 1).
- `-c`, `--context`: Enable prompt caching/rolling context to help the model maintain sentence continuity across chunks.
transcribe.py
+106 -3
View File
@@ -1,10 +1,12 @@
import sys
import time
import re
import requests
import threading
import json
import argparse
from unittest.mock import MagicMock
from collections import Counter
# Comprehensive workaround for missing _lzma in some Python builds
try:
@@ -45,11 +47,38 @@ CHANNELS = 1
SAMPLERATE = 16000
BLOCK_SIZE = 512
VAD_THRESHOLD = 0.5
BUFFER_LIMIT = SAMPLERATE * 30
audio_queue = queue.Queue()
ingest_queue = queue.Queue()
def is_hallucination(text):
"""Detect common Whisper hallucinations or high repetition."""
if not text: return False
# Common hallucinations
hallucinations = [
r"thanks? for watching",
r"please subscribe",
r"youtube",
r"click the link",
r"like and subscribe",
r"tuned in",
r"next time",
]
for pattern in hallucinations:
if re.search(pattern, text, re.IGNORECASE):
return True
# Check for excessive word repetition (e.g. "Hallelujah" repeated 10 times)
words = text.lower().split()
if len(words) >= 8:
counts = Counter(words)
most_common_word, count = counts.most_common(1)[0]
if count / len(words) > 0.75:
return True
return False
def callback(indata, frames, time, status):
if status:
print(status, file=sys.stderr)
@@ -91,11 +120,13 @@ def main():
parser.add_argument("-i", "--ingest", action="store_true", help="Enable data transmission to server")
parser.add_argument("-l", "--list-devices", action="store_true", help="Show available audio devices and exit")
parser.add_argument("-d", "--device", type=int, help="Input device index")
parser.add_argument("--loopback", action="store_true", help="Automatically select a loopback device (e.g., BlackHole, Stereo Mix)")
parser.add_argument("-q", "--quantize", action="store_true", help="Use 4-bit quantized Whisper model for speed")
parser.add_argument("-s", "--stream", action="store_true", help="Enable real-time streaming transcription (Draft mode)")
parser.add_argument("-c", "--context", action="store_true", help="Enable prompt caching/rolling context for better continuity")
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)")
args = parser.parse_args()
@@ -107,6 +138,7 @@ def main():
print(sd.query_devices())
return
buffer_limit = SAMPLERATE * args.max_buffer
device = "mps" if torch.backends.mps.is_available() else "cpu"
print(f"Using device: {device}")
@@ -132,9 +164,28 @@ def main():
print("Models loaded.")
# 2. Select Audio Device
device_index = None
if args.device is not None:
device_index = args.device
else:
elif args.loopback:
print("Searching for loopback device...")
devices = sd.query_devices()
for i, dev in enumerate(devices):
name = dev['name'].lower()
if any(keyword in name for keyword in ["blackhole", "loopback", "soundflower", "stereo mix"]):
if dev['max_input_channels'] > 0:
device_index = i
print(f"Using loopback device: {dev['name']} (Index {i})")
print("\n[TIP] For macOS with BlackHole:")
print("1. Open 'Audio MIDI Setup' and create a 'Multi-Output Device'.")
print("2. Select your speakers AND 'BlackHole 2ch'.")
print("3. Set your system output to this 'Multi-Output Device'.")
print("This way you can hear the audio while it is being captioned.\n")
break
if device_index is None:
print("No loopback device found. Falling back to default.")
if device_index is None and not args.loopback:
print("\nAvailable Audio Devices:")
print(sd.query_devices())
try:
@@ -144,12 +195,19 @@ def main():
print("Invalid input, using default device.")
device_index = None
if device_index is not None:
print(f"Using device index: {device_index}")
else:
print("Using system default input device.")
print(f"\nStarting live transcription{' & server ingest' if args.ingest else ''}... (Press Ctrl+C to stop)")
audio_buffer = []
speech_started = False
last_stream_time = time.time()
last_change_time = time.time()
rolling_context = ""
last_draft_text = ""
try:
with sd.InputStream(samplerate=SAMPLERATE, channels=CHANNELS, callback=callback, blocksize=BLOCK_SIZE, device=device_index):
@@ -161,6 +219,7 @@ def main():
if len(audio_buffer) > 0:
current_audio = np.concatenate(audio_buffer)
audio_tensor = torch.from_numpy(current_audio)
buffer_duration = len(current_audio) / SAMPLERATE
speech_timestamps = get_speech_timestamps(
audio_tensor,
@@ -170,12 +229,28 @@ def main():
min_silence_duration_ms=args.silence
)
# --- STUCK WATCHDOG ---
# If buffer is getting long (>12s) and we haven't had a change in draft for 7s,
# OR if buffer is extremely long (>25s) regardless of draft activity.
time_since_last_change = time.time() - last_change_time
if (buffer_duration > 12.0 and time_since_last_change > 7.0) or (buffer_duration > 25.0):
print(f"\n[SYSTEM]: Transcription watchdog triggered (Buffer: {buffer_duration:.1f}s, No change: {time_since_last_change:.1f}s). Resetting...")
audio_buffer = []
speech_started = False
rolling_context = ""
last_draft_text = ""
last_change_time = time.time()
if args.stream:
sys.stdout.write("\r\033[K")
sys.stdout.flush()
continue
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 * args.silence / 1000) or buffer_len_samples > BUFFER_LIMIT:
if (buffer_len_samples - last_end) > (SAMPLERATE * args.silence / 1000) or buffer_len_samples > buffer_limit:
# Clear draft line if it was used
if args.stream:
@@ -194,11 +269,18 @@ def main():
original_text = transcription_result['text'].strip()
detected_lang = transcription_result.get('language', args.lang if args.lang else 'en')
if is_hallucination(original_text):
print(f"\n[SYSTEM]: Hallucination detected, ignoring and resetting context.")
original_text = ""
rolling_context = ""
if original_text:
print(f"\n[{detected_lang.upper()}]: {original_text}")
last_change_time = time.time() # Successfully transcribed full segment
# Prepare payload
payload = {"original": original_text}
# ... (payload construction)
# Include detected language if requested or if it's the bridge
if (detected_lang in active_target_langs) or (detected_lang == "en" and args.en):
@@ -251,6 +333,8 @@ def main():
audio_buffer = []
speech_started = False
last_stream_time = time.time()
stuck_draft_count = 0
last_draft_text = ""
elif args.stream and (time.time() - last_stream_time) > 1.0:
# Draft transcription
@@ -260,13 +344,32 @@ def main():
draft_result = mlx_whisper.transcribe(current_audio, **draft_kwargs)
draft_text = draft_result['text'].strip()
if draft_text:
# Update timestamp ONLY if the text actually changed
if draft_text != last_draft_text:
last_change_time = time.time()
last_draft_text = draft_text
sys.stdout.write(f"\r\033[K[DRAFT]: {draft_text}")
sys.stdout.flush()
# Send draft to ingest if enabled
if args.ingest:
ingest_queue.put({"draft": draft_text})
else:
# If Whisper returns empty, check if we've been silent for too long
# even though VAD says there is speech.
if buffer_duration > 10.0 and (time.time() - last_change_time) > 7.0:
print(f"\n[SYSTEM]: Draft is empty while audio continues. Forcing reset...")
audio_buffer = []
speech_started = False
rolling_context = ""
last_change_time = time.time()
last_draft_text = ""
sys.stdout.write("\r\033[K")
sys.stdout.flush()
continue
last_stream_time = time.time()