vad.py

from abc import ABC, abstractmethod
from collections import Counter
from dis import dis
from typing import Any, Callable, Iterator, List, Dict, Union

from pprint import pprint
import torch

import ffmpeg
import numpy as np

# Defaults
SPEECH_TRESHOLD = 0.3
MAX_SILENT_PERIOD = 10 # seconds
SEGMENT_PADDING_LEFT = 1 # Start detected text segment early
SEGMENT_PADDING_RIGHT = 4 # End detected segments late


class AbstractTranscription(ABC):
    def __init__(self, segment_padding_left: int = None, segment_padding_right = None, max_silent_period: int = None):
        self.sampling_rate = 16000
        self.segment_padding_left = segment_padding_left
        self.segment_padding_right = segment_padding_right
        self.max_silent_period = max_silent_period

    def get_audio_segment(self, str, start_time: str = None, duration: str = None):
        return load_audio(str, self.sampling_rate, start_time, duration)

    @abstractmethod
    def get_transcribe_timestamps(self, audio: str):
        """
        Get the start and end timestamps of the sections that should be transcribed by this VAD method.

        Parameters
        ----------
        audio: str
            The audio file.

        Returns
        -------
        A list of start and end timestamps, in fractional seconds.
        """
        return 

    def transcribe(self, audio: str, whisperCallable: Callable[[Union[str, np.ndarray, torch.Tensor]], dict[str, Union[dict, Any]]]):
        # get speech timestamps from full audio file
        seconds_timestamps = self.get_transcribe_timestamps(audio)

        padded = self.pad_timestamps(seconds_timestamps, self.segment_padding_left, self.segment_padding_right)
        merged = self.merge_timestamps(padded, self.max_silent_period)

        print("Timestamps:")
        pprint(merged)

        result = {
            'text': "",
            'segments': [],
            'language': ""
        }
        languageCounter = Counter()

        # For each time segment, run whisper
        for segment in merged:
            segment_start = segment['start']
            segment_duration = segment['end'] - segment_start

            segment_audio = self.get_audio_segment(audio, start_time = str(segment_start) + "s", duration = str(segment_duration) + "s")

            print("Running whisper on " + str(segment_start) + ", duration: " + str(segment_duration))
            segment_result = whisperCallable(segment_audio)
            adjusted_segments = self.adjust_whisper_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration)

            # Append to output
            result['text'] += segment_result['text']
            result['segments'].extend(adjusted_segments)

            # Increment detected language
            languageCounter[segment_result['language']] += 1

        if len(languageCounter) > 0:
            result['language'] = languageCounter.most_common(1)[0][0]

        return result
            
    def adjust_whisper_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None):
        result = []

        for segment in segments:
            segment_start = float(segment['start'])
            segment_end = float(segment['end'])

            # Filter segments?
            if (max_source_time is not None):
                if (segment_start > max_source_time):
                    continue
                segment_end = min(max_source_time, segment_end)

                new_segment = segment.copy()

            # Add to start and end
            new_segment['start'] = segment_start + adjust_seconds
            new_segment['end'] = segment_end + adjust_seconds
            result.append(new_segment)
        return result

    def pad_timestamps(self, timestamps: List[Dict[str, Any]], padding_left: float, padding_right: float):
        result = []

        for entry in timestamps:
            segment_start = entry['start']
            segment_end = entry['end']

            if padding_left is not None:
                segment_start = max(0, segment_start - padding_left)
            if padding_right is not None:
                segment_end = segment_end + padding_right

            result.append({ 'start': segment_start, 'end': segment_end })

        return result

    def merge_timestamps(self, timestamps: List[Dict[str, Any]], max_distance: float):
        if max_distance is None:
            return timestamps

        result = []
        current_entry = None

        for entry in timestamps:
            if current_entry is None:
                current_entry = entry
                continue

            # Get distance to the previous entry
            distance = entry['start'] - current_entry['end']

            if distance <= max_distance:
                # Merge
                current_entry['end'] = entry['end']
            else:
                # Output current entry
                result.append(current_entry)
                current_entry = entry
        
        # Add final entry
        if current_entry is not None:
            result.append(current_entry)

        return result

    def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float):
        result = []

        for entry in timestamps:
            start = entry['start']
            end = entry['end']

            result.append({
                'start': start * factor,
                'end': end * factor
            })
        return result

class VadSileroTranscription(AbstractTranscription):
    def __init__(self):
        super().__init__(SEGMENT_PADDING_LEFT, SEGMENT_PADDING_RIGHT, MAX_SILENT_PERIOD)
        self.model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')

        (self.get_speech_timestamps, _, _, _, _) = utils

    def get_transcribe_timestamps(self, audio: str):
        wav = self.get_audio_segment(audio)

        sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD)
        seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate) 

        return seconds_timestamps

# A very simple VAD that just marks every N seconds as speech
class VadPeriodicTranscription(AbstractTranscription):
    def __init__(self, periodic_duration: int):
        super().__init__()
        self.periodic_duration = periodic_duration

    def get_transcribe_timestamps(self, audio: str):
        # Get duration in seconds
        audio_duration = float(ffmpeg.probe(audio)["format"]["duration"])
        result = []

        # Generate a timestamp every N seconds
        start_timestamp = 0

        while (start_timestamp < audio_duration):
            end_timestamp = min(start_timestamp + self.periodic_duration, audio_duration)
            segment_duration = end_timestamp - start_timestamp

            # Minimum duration is 1 second
            if (segment_duration >= 1):
                result.append( {  'start': start_timestamp, 'end': end_timestamp } )

            start_timestamp = end_timestamp

        return result

def load_audio(file: str, sample_rate: int = 16000, 
               start_time: str = None, duration: str = None):
    """
    Open an audio file and read as mono waveform, resampling as necessary

    Parameters
    ----------
    file: str
        The audio file to open

    sr: int
        The sample rate to resample the audio if necessary

    start_time: str
        The start time, using the standard FFMPEG time duration syntax, or None to disable.
    
    duration: str
        The duration, using the standard FFMPEG time duration syntax, or None to disable.

    Returns
    -------
    A NumPy array containing the audio waveform, in float32 dtype.
    """
    try:
        inputArgs = {'threads': 0}

        if (start_time is not None):
            inputArgs['ss'] = start_time
        if (duration is not None):
            inputArgs['t'] = duration

        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
        out, _ = (
            ffmpeg.input(file, **inputArgs)
            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate)
            .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True)
        )
    except ffmpeg.Error as e:
        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}")

    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0