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feat: add hotwords support for vLLM ASR

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This commit is contained in:
YingboHAO
2026-02-04 10:33:20 +00:00
parent 0aa8cb4c64
commit bb54f78d0e
5 changed files with 253 additions and 137 deletions
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docs/vibevoice-vllm-asr.md
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@@ -52,15 +52,25 @@ docker logs -f vibevoice-vllm
Once the vLLM server is running, test it with the provided script:
```bash
# Run the test (use container path /app/...)
# Basic transcription
docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api.py /app/audio.wav
# With hotwords for better recognition of specific terms
docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api.py /app/audio.wav --hotwords "Microsoft,VibeVoice"
```
```bash
# Run the recover_test (use container path /app/...)
# With auto-recovery from repetition loops (for long audio)
docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api_auto_recover.py /app/audio.wav
# Auto-recover with hotwords
docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api_auto_recover.py /app/audio.wav --hotwords "Microsoft,VibeVoice"
```
> **Note**: The audio file must be inside the mounted directory (`/app` in the container). Copy your audio to the VibeVoice folder before testing.
> **Note**:
> - The audio/video file must be inside the mounted directory (`/app` in the container). Copy your files to the VibeVoice folder before testing.
> - Hotwords help improve recognition of domain-specific terms like proper nouns, technical terms, and speaker names.
### Environment Variables
vllm_plugin/tests/52min.mp3
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vllm_plugin/tests/test_api.py
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@@ -1,14 +1,23 @@
#!/usr/bin/env python3
"""
Test VibeVoice vLLM API with Streaming (Real-time output).
Test VibeVoice vLLM API with Streaming and Optional Hotwords Support.
This script tests ASR transcription via the vLLM OpenAI-compatible API.
By default, it runs standard transcription without hotwords.
Optionally, you can provide hotwords (context_info) to improve recognition
of domain-specific content like proper nouns, technical terms, and speaker names.
Hotwords are embedded in the prompt as "with extra info: {hotwords}".
Usage:
python test_api.py [audio_path] [--url URL]
python test_api_with_hotwords.py [audio_path] [--url URL] [--hotwords "word1,word2"]
Examples:
python test_api.py # Use default audio
python test_api.py /path/to/audio.wav # Specify audio file
python test_api.py /path/to/audio.mp3 --url http://localhost:8000 # Custom URL
# Standard transcription (no hotwords)
python3 test_api.py audio.wav
# With hotwords for better recognition of specific terms
python3 test_api.py audio.wav --hotwords "Microsoft,Azure,VibeVoice"
"""
import requests
import json
@@ -21,38 +30,38 @@ import argparse
def _guess_mime_type(path: str) -> str:
"""Guess MIME type from file extension."""
ext = os.path.splitext(path)[1].lower()
if ext == ".wav":
return "audio/wav"
if ext in (".mp3",):
return "audio/mpeg"
if ext in (".m4a",):
return "audio/mp4"
if ext in (".mp4", ".m4v", ".mov", ".webm"):
return "video/mp4"
if ext in (".flac",):
return "audio/flac"
if ext in (".ogg", ".opus"):
return "audio/ogg"
return "application/octet-stream"
mime_map = {
".wav": "audio/wav",
".mp3": "audio/mpeg",
".m4a": "audio/mp4",
".mp4": "video/mp4",
".flac": "audio/flac",
".ogg": "audio/ogg",
".opus": "audio/ogg",
}
return mime_map.get(ext, "application/octet-stream")
def _get_duration_seconds_ffprobe(path: str) -> float:
"""Get audio duration using ffprobe."""
cmd = [
"ffprobe",
"-v",
"error",
"-show_entries",
"format=duration",
"-of",
"default=noprint_wrappers=1:nokey=1",
"ffprobe", "-v", "error",
"-show_entries", "format=duration",
"-of", "default=noprint_wrappers=1:nokey=1",
path,
]
out = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode("utf-8").strip()
return float(out)
def _is_video_file(path: str) -> bool:
"""Check if the file is a video file that needs audio extraction."""
ext = os.path.splitext(path)[1].lower()
return ext in (".mp4", ".m4v", ".mov", ".webm", ".avi", ".mkv")
def _extract_audio_from_video(video_path: str) -> str:
"""
Extract audio from video file (mp4/mov/webm) to a temporary mp3 file.
@@ -74,26 +83,40 @@ def _extract_audio_from_video(video_path: str) -> str:
return audio_path
def _is_video_file(path: str) -> bool:
"""Check if the file is a video file that needs audio extraction."""
ext = os.path.splitext(path)[1].lower()
return ext in (".mp4", ".m4v", ".mov", ".webm", ".avi", ".mkv")
def test_transcription(audio_path: str, base_url: str = "http://localhost:8000"):
"""Test ASR transcription with streaming output."""
def test_transcription_with_hotwords(
audio_path: str,
context_info: str = None,
base_url: str = "http://localhost:8000",
):
"""
Test ASR transcription with customized hotwords.
print(f"Loading audio from: {audio_path}")
Hotwords are embedded in the prompt text as "with extra info: {hotwords}".
This helps the model recognize domain-specific terms more accurately.
Args:
audio_path: Path to the audio file
context_info: Hotwords string (e.g., "Microsoft,Azure,VibeVoice")
base_url: vLLM server URL
"""
print(f"=" * 70)
print(f"Testing Customized Hotwords Support")
print(f"=" * 70)
print(f"Input file: {audio_path}")
print(f"Hotwords: {context_info or '(none)'}")
print()
# Handle video files: extract audio first
temp_audio_path = None
actual_audio_path = audio_path
if _is_video_file(audio_path):
print(f"Detected video file, extracting audio...")
print(f"🎬 Detected video file, extracting audio...")
temp_audio_path = _extract_audio_from_video(audio_path)
actual_audio_path = temp_audio_path
print(f"Audio extracted to: {temp_audio_path}")
print(f"✅ Audio extracted to: {temp_audio_path}")
# Load audio
try:
duration = _get_duration_seconds_ffprobe(actual_audio_path)
print(f"Audio duration: {duration:.2f} seconds")
@@ -106,16 +129,30 @@ def test_transcription(audio_path: str, base_url: str = "http://localhost:8000")
except Exception as e:
print(f"Error preparing audio: {e}")
# Cleanup temp file if created
if temp_audio_path and os.path.exists(temp_audio_path):
os.remove(temp_audio_path)
return
# Build the request
url = f"{base_url}/v1/chat/completions"
show_keys = ["Start time", "End time", "Speaker ID", "Content"]
prompt_text = (
f"This is a {duration:.2f} seconds audio, please transcribe it with these keys: "
+ ", ".join(show_keys)
)
# Build prompt with optional hotwords
# Hotwords are embedded as "with extra info: {hotwords}" in the prompt
if context_info and context_info.strip():
prompt_text = (
f"This is a {duration:.2f} seconds audio, with extra info: {context_info.strip()}\n\n"
f"Please transcribe it with these keys: " + ", ".join(show_keys)
)
print(f"\n📝 Hotwords embedded in prompt: '{context_info}'")
else:
prompt_text = (
f"This is a {duration:.2f} seconds audio, please transcribe it with these keys: "
+ ", ".join(show_keys)
)
print(f"\n📝 No hotwords provided")
mime = _guess_mime_type(actual_audio_path)
data_url = f"data:{mime};base64,{audio_b64}"
@@ -139,20 +176,19 @@ def test_transcription(audio_path: str, base_url: str = "http://localhost:8000")
"temperature": 0.0,
"stream": True,
"top_p": 1.0,
"repetition_penalty": 1.0,
}
print(f"\nSending request to {url} (Streaming Mode)...")
print(f"Prompt: {prompt_text}")
print("-" * 60)
print(f"\n{'=' * 70}")
print(f"Sending request to {url}")
print(f"{'=' * 70}")
t0 = time.time()
try:
response = requests.post(url, json=payload, stream=True, timeout=12000)
if response.status_code == 200:
print("Response received. Streaming content:\n")
print("\n✅ Response received. Streaming content:\n")
print("-" * 50)
printed = ""
for line in response.iter_lines():
@@ -162,92 +198,72 @@ def test_transcription(audio_path: str, base_url: str = "http://localhost:8000")
if decoded_line.startswith("data: "):
json_str = decoded_line[6:]
if json_str.strip() == "[DONE]":
print("\n\n[Finished]")
print("\n" + "-" * 50)
print("✅ [Finished]")
break
try:
data = json.loads(json_str)
delta = data['choices'][0]['delta']
content = delta.get('content', '')
if content:
# vLLM/OpenAI-compatible streams may emit either
# incremental deltas OR the full accumulated text.
# Only print the newly-added part to avoid repeats.
if content.startswith(printed):
to_print = content[len(printed):]
else:
to_print = content
if to_print:
print(to_print, end='', flush=True)
printed += to_print
except json.JSONDecodeError:
pass
else:
print(f"Error: {response.status_code}")
print(f"❌ Error: {response.status_code}")
print(response.text)
except requests.exceptions.Timeout:
print("\nRequest timed out!")
print("❌ Request timed out!")
except Exception as e:
print(f"\nError: {e}")
print(f"❌ Error: {e}")
print(f"\n{'-'*60}")
print(f"Total time elapsed: {time.time() - t0:.2f}s")
elapsed = time.time() - t0
print(f"\n{'=' * 70}")
print(f"⏱️ Total time elapsed: {elapsed:.2f}s")
print(f"📊 RTF (Real-Time Factor): {elapsed / duration:.2f}x")
print(f"{'=' * 70}")
# Cleanup temp audio file if created
if temp_audio_path and os.path.exists(temp_audio_path):
os.remove(temp_audio_path)
print(f"Cleaned up temp file: {temp_audio_path}")
print(f"🗑️ Cleaned up temp file: {temp_audio_path}")
def main():
parser = argparse.ArgumentParser(
description="Test VibeVoice vLLM API with streaming output"
description="Test VibeVoice vLLM API with Customized Hotwords"
)
parser.add_argument(
"audio_path",
nargs="?",
default=None,
help="Path to audio file (wav, mp3, flac, etc.) or video file"
)
parser.add_argument(
"--url",
default="http://localhost:8000",
help="vLLM server base URL (default: http://localhost:8000)"
help="vLLM server URL (default: http://localhost:8000)"
)
parser.add_argument(
"--hotwords",
type=str,
default=None,
help="Hotwords to improve recognition (e.g., 'Microsoft,Azure,VibeVoice')"
)
args = parser.parse_args()
# Find default audio if not specified
audio_path = args.audio_path
if audio_path is None:
# Try to find a sample audio in common locations
possible_paths = [
# In VibeVoice demo folder
os.path.join(os.path.dirname(__file__), "..", "..", "demo", "voices", "en-Carter_man.wav"),
os.path.join(os.path.dirname(__file__), "..", "..", "demo", "voices", "zh-Anchen_man_bgm.wav"),
# Relative to current directory
"demo/voices/en-Carter_man.wav",
"demo/voices/zh-Anchen_man_bgm.wav",
]
for path in possible_paths:
if os.path.exists(path):
audio_path = path
break
if audio_path is None:
print("Error: No audio file specified and no default audio found.")
print("Usage: python test_api.py <audio_path>")
sys.exit(1)
if not os.path.exists(audio_path):
print(f"Error: Audio file not found: {audio_path}")
sys.exit(1)
test_transcription(audio_path, args.url)
# Run test
test_transcription_with_hotwords(
audio_path=args.audio_path,
context_info=args.hotwords,
base_url=args.url,
)
if __name__ == "__main__":
vllm_plugin/tests/test_api_auto_recover.py
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@@ -1,18 +1,36 @@
#!/usr/bin/env python3
"""
VibeVoice vLLM API with Auto-Recovery from Repetition Loops.
Test VibeVoice vLLM API with Streaming, Hotwords, and Auto-Recovery.
Strategy:
1. Start with greedy decoding (temperature=0, top_p=1.0)
2. Stream and detect repetition patterns in real-time
3. Only output content up to (current_length - window_size) at segment boundaries
4. When loop detected:
- Truncate to last complete segment boundary (},)
- Recovery with temperature=0.2/0.3/0.4 for retry 1/2/3, top_p=0.95
5. Max 3 retries, if all fail output error message
This script tests ASR transcription with automatic recovery from repetition loops.
Supports optional hotwords to improve recognition of domain-specific terms.
User sees: clean streaming transcription output (only complete segments)
Internal: automatic recovery from repetition loops (silent)
Features:
- Streaming output with real-time repetition detection
- Auto-recovery when model enters repetition loops
- Optional hotwords support (embedded in prompt as "with extra info: {hotwords}")
- Video file support (auto-extracts audio)
Recovery Strategy:
1. First attempt: greedy decoding (temperature=0, top_p=1.0)
2. If loop detected: retry with temperature=0.2/0.3/0.4, top_p=0.95
3. Max 3 retries, truncate to last complete segment boundary
Usage:
python test_api_auto_recover.py <audio_path> [output_path] [--url URL] [--hotwords "word1,word2"] [--debug]
Examples:
# Basic usage
python3 test_api_auto_recover.py audio.wav
# With hotwords
python3 test_api_auto_recover.py audio.wav --hotwords "Microsoft,VibeVoice"
# Save result to file
python3 test_api_auto_recover.py audio.wav result.txt
# Debug mode (show recovery info)
python3 test_api_auto_recover.py audio.wav --debug
"""
import requests
import json
@@ -22,6 +40,7 @@ import sys
import os
import subprocess
import re
import argparse
from collections import Counter
@@ -441,30 +460,41 @@ def stream_with_recovery(
return None
def test_transcription_with_recovery():
"""Main test function with auto-recovery."""
def test_transcription_with_recovery(
audio_path: str,
output_path: str = None,
base_url: str = "http://localhost:8000",
hotwords: str = None,
debug: bool = False,
):
"""
Test ASR transcription with auto-recovery from repetition loops.
# Parse arguments
debug = "--debug" in sys.argv or "-debug" in sys.argv
args = [a for a in sys.argv[1:] if not a.startswith("-")]
Args:
audio_path: Path to the audio file
output_path: Optional path to save transcription result
base_url: vLLM server URL
hotwords: Hotwords string (e.g., "Microsoft,Azure,VibeVoice")
debug: Show recovery debug info
"""
audio_path = (
args[0]
)
output_path = args[1] if len(args) > 1 else None
print(f"Loading audio from: {audio_path}")
print(f"=" * 70)
print(f"Testing with Auto-Recovery")
print(f"=" * 70)
print(f"Input file: {audio_path}")
print(f"Hotwords: {hotwords or '(none)'}")
print()
# Handle video files: extract audio first
temp_audio_path = None
actual_audio_path = audio_path
if _is_video_file(audio_path):
print(f"Detected video file, extracting audio...")
print(f"🎬 Detected video file, extracting audio...")
temp_audio_path = _extract_audio_from_video(audio_path)
actual_audio_path = temp_audio_path
print(f"Audio extracted to: {temp_audio_path}")
print(f"✅ Audio extracted to: {temp_audio_path}")
# Load audio
try:
duration = _get_duration_seconds_ffprobe(actual_audio_path)
print(f"Audio duration: {duration:.2f} seconds")
@@ -476,16 +506,29 @@ def test_transcription_with_recovery():
print(f"Audio size: {len(audio_bytes)} bytes")
except Exception as e:
print(f"Error preparing audio: {e}")
print(f"❌ Error preparing audio: {e}")
# Cleanup temp file if created
if temp_audio_path and os.path.exists(temp_audio_path):
os.remove(temp_audio_path)
return
url = "http://localhost:8000/v1/chat/completions"
url = f"{base_url}/v1/chat/completions"
show_keys = ["Start time", "End time", "Speaker ID", "Content"]
prompt_text = (
f"This is a {duration:.2f} seconds audio, please transcribe it with these keys: "
+ ", ".join(show_keys)
)
# Build prompt with optional hotwords
if hotwords and hotwords.strip():
prompt_text = (
f"This is a {duration:.2f} seconds audio, with extra info: {hotwords.strip()}\n\n"
f"Please transcribe it with these keys: " + ", ".join(show_keys)
)
print(f"\n📝 Hotwords embedded in prompt: '{hotwords}'")
else:
prompt_text = (
f"This is a {duration:.2f} seconds audio, please transcribe it with these keys: "
+ ", ".join(show_keys)
)
print(f"\n📝 No hotwords provided")
mime = _guess_mime_type(actual_audio_path)
data_url = f"data:{mime};base64,{audio_b64}"
@@ -505,12 +548,13 @@ def test_transcription_with_recovery():
}
]
print(f"\nSending request to {url} (Streaming Mode)...")
print(f"Prompt: {prompt_text}")
print("-" * 60)
print("Response received. Streaming content:\n")
print(f"\n{'=' * 70}")
print(f"Sending request to {url}")
print(f"{'=' * 70}")
t0 = time.time()
print("\n✅ Response received. Streaming content:\n")
print("-" * 50)
result = stream_with_recovery(
url=url,
@@ -522,27 +566,73 @@ def test_transcription_with_recovery():
debug=debug,
)
print("\n[Finished]")
print("-" * 60)
print(f"Total time elapsed: {time.time() - t0:.2f}s")
elapsed = time.time() - t0
print("-" * 50)
print("✅ [Finished]")
print(f"\n{'=' * 70}")
print(f"⏱️ Total time elapsed: {elapsed:.2f}s")
print(f"{'=' * 70}")
if result is None:
print("Transcription failed")
print("❌ Transcription failed")
return
print(f"Final output length: {len(result)} chars")
print(f"📄 Final output length: {len(result)} chars")
# Optionally save result
if output_path:
with open(output_path, "w", encoding="utf-8") as f:
f.write(result)
print(f"Result saved to: {output_path}")
print(f"💾 Result saved to: {output_path}")
# Cleanup temp audio file if created
if temp_audio_path and os.path.exists(temp_audio_path):
os.remove(temp_audio_path)
print(f"Cleaned up temp file: {temp_audio_path}")
print(f"🗑️ Cleaned up temp file: {temp_audio_path}")
def main():
parser = argparse.ArgumentParser(
description="Test VibeVoice vLLM API with auto-recovery from repetition loops"
)
parser.add_argument(
"audio_path",
help="Path to audio file (wav, mp3, flac, etc.) or video file"
)
parser.add_argument(
"output_path",
nargs="?",
default=None,
help="Optional path to save transcription result"
)
parser.add_argument(
"--url",
default="http://localhost:8000",
help="vLLM server URL (default: http://localhost:8000)"
)
parser.add_argument(
"--hotwords",
type=str,
default=None,
help="Hotwords to improve recognition (e.g., 'Microsoft,Azure,VibeVoice')"
)
parser.add_argument(
"--debug",
action="store_true",
help="Show recovery debug info"
)
args = parser.parse_args()
# Run test
test_transcription_with_recovery(
audio_path=args.audio_path,
output_path=args.output_path,
base_url=args.url,
hotwords=args.hotwords,
debug=args.debug,
)
if __name__ == "__main__":
test_transcription_with_recovery()
main()
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