speech-detect 0.1.0

A Python library for detecting speech segments and non-speech gaps in audio/video files using FSMN-VAD-ONNX with streaming processing

speech-detect

A Python library for detecting speech segments and non-speech gaps in audio/video files using FSMN-VAD-ONNX with streaming processing.

Features

• Streaming VAD detection: Process large audio/video files in chunks without loading everything into memory
• Speech segment detection: Detect all speech segments in audio/video files
• Non-speech gap derivation: Compute non-speech gaps from speech segments
• Format support: Supports all audio/video formats that FFmpeg supports (MP3, WAV, FLAC, Opus, MP4, etc.)
• Time range support: Support start time and duration parameters for partial processing
• Memory efficient: Constant memory usage regardless of audio file duration

Installation

pip install speech-detect

Note: This package requires:

• FFmpeg to be installed on your system and available in PATH
• FSMN-VAD-ONNX model files (see Model Setup below)

Model Setup

This package requires FSMN-VAD-ONNX model files. The model is available on Hugging Face:

Model Repository: funasr/fsmn-vad-onnx

Download the Model

1. Install Git LFS (required for downloading large model files):

   git lfs install
   

2. Clone the model repository:

   git clone https://huggingface.co/funasr/fsmn-vad-onnx
   

   This will download the model files including model_quant.onnx, config.yaml, am.mvn, etc.

3. Set the MODEL_FSMN_VAD_DIR environment variable to point to the model directory:

   export MODEL_FSMN_VAD_DIR=/path/to/fsmn-vad-onnx
   

Alternatively, you can specify the model directory when initializing SpeechDetector:

from speech_detect import SpeechDetector

detector = SpeechDetector(model_dir="/path/to/fsmn-vad-onnx")

Quick Start

Detect Speech Segments and Gaps

from speech_detect import SpeechDetector

# Initialize detector (reads MODEL_FSMN_VAD_DIR from environment)
detector = SpeechDetector()

# Detect speech segments and non-speech gaps in an audio file
speech_segments, gaps = detector.detect("audio.mp3")

# speech_segments is a list of dictionaries: [{"start": 0, "end": 500}, ...]
for segment in speech_segments:
    start_ms = segment["start"]
    end_ms = segment["end"]
    duration = end_ms - start_ms
    print(f"Speech segment: {start_ms}ms - {end_ms}ms (duration: {duration}ms)")

# gaps is a list of dictionaries: [{"start": 0, "end": 500}, ...]
for gap in gaps:
    start_ms = gap["start"]
    end_ms = gap["end"]
    duration = end_ms - start_ms
    print(f"Non-speech gap: {start_ms}ms - {end_ms}ms (duration: {duration}ms)")

Processing Specific Time Range

# Process only the first 30 seconds
speech_segments, gaps = detector.detect(
    file_path="audio.mp3",
    start_ms=0,
    duration_ms=30000,
)

# Process from 10 seconds, duration 5 seconds
speech_segments, gaps = detector.detect(
    file_path="audio.mp3",
    start_ms=10000,
    duration_ms=5000,
)

Custom Chunk Size

# Use 1-minute chunks instead of default 20-minute chunks
speech_segments, gaps = detector.detect(
    file_path="audio.mp3",
    chunk_duration_sec=60,
)

API Reference

SpeechDetector

Main class for speech detection. All methods are instance methods.

SpeechDetector.__init__(model_dir=None)

Initialize speech detector.

Parameters:

• model_dir (str, optional): Path to the FSMN-VAD model directory. If None, reads from MODEL_FSMN_VAD_DIR environment variable.

Note: The FSMN-VAD model only has a quantized version, so quantize=True is always used internally.

Raises:

• VadModelNotFoundError: If model directory is not found or not set
• VadModelInitializationError: If model initialization fails

SpeechDetector.detect(file_path, chunk_duration_sec=None, start_ms=None, duration_ms=None)

Detect speech segments in audio/video file using streaming processing.

Parameters:

• file_path (str): Path to the audio/video file (supports all FFmpeg formats)
• chunk_duration_sec (int, optional): Duration of each chunk in seconds. Defaults to 1200 (20 minutes). Must be > 0 if provided.
• start_ms (int, optional): Start position in milliseconds. None means from file beginning. If None but duration_ms is provided, defaults to 0.
• duration_ms (int, optional): Total duration to process in milliseconds. None means process until end. If specified, processing stops when this duration is reached.

Returns:

• tuple[list[VadSegment], list[VadSegment]]: Tuple of (speech_segments, gaps)
  • speech_segments: List of speech segments, format: [{"start": ms, "end": ms}, ...]
    • Timestamps are relative to audio start (from 0)
    • Unit: milliseconds
  • gaps: List of non-speech gaps, format: [{"start": ms, "end": ms}, ...]
    • Timestamps are relative to audio start (from 0)
    • Unit: milliseconds

Raises:

• VadProcessingError: If processing fails

Data Types

VadSegment

A TypedDict representing a time segment (can be a speech segment or a non-speech gap).

Fields:

• start (int): Segment start time in milliseconds
• end (int): Segment end time in milliseconds

Example:

segment: VadSegment = {"start": 100, "end": 500}

Exceptions

VadModelNotFoundError

Raised when VAD model directory is not found or not set.

Attributes:

• message: Human-readable error message

VadModelInitializationError

Raised when VAD model initialization fails.

Attributes:

• message: Primary error message
• model_dir: Path to the model directory that caused the error

VadProcessingError

Raised when VAD processing fails.

Attributes:

• message: Primary error message
• file_path: Path to the file being processed
• details: Additional error details dictionary

Requirements

• Python >= 3.10
• FFmpeg (must be installed separately)
• numpy >= 1.26.4
• funasr-onnx >= 0.4.1
• ffmpeg-audio >= 0.1.3

License

MIT License