from abc import ABC, abstractmethod from collections import Counter from dis import dis from typing import Any, Iterator, List, Dict from pprint import pprint # Workaround for https://github.com/tensorflow/tensorflow/issues/48797 try: import tensorflow as tf except ModuleNotFoundError: # Error handling pass import torch import ffmpeg import numpy as np from utils import format_timestamp # Defaults for Silero # TODO: Make these configurable? SPEECH_TRESHOLD = 0.3 MAX_SILENT_PERIOD = 10 # seconds MAX_MERGE_SIZE = 150 # Do not create segments larger than 2.5 minutes SEGMENT_PADDING_LEFT = 1 # Start detected text segment early SEGMENT_PADDING_RIGHT = 3 # End detected segments late # Whether to attempt to transcribe non-speech TRANSCRIBE_NON_SPEECH = False # Minimum size of segments to process MIN_SEGMENT_DURATION = 1 class AbstractTranscription(ABC): def __init__(self, segment_padding_left: int = None, segment_padding_right = None, max_silent_period: int = None, max_merge_size: int = None, transcribe_non_speech: bool = False): 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 self.max_merge_size = max_merge_size self.transcribe_non_speech = transcribe_non_speech 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): """ Transcribe the given audo file. Parameters ---------- audio: str The audio file. whisperCallable: Callable[[Union[str, np.ndarray, torch.Tensor]], dict[str, Union[dict, Any]]] The callback that is used to invoke Whisper on an audio file/buffer. Returns ------- A list of start and end timestamps, in fractional seconds. """ # 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, self.max_merge_size) print("Timestamps:") pprint(merged) if self.transcribe_non_speech: max_audio_duration = float(ffmpeg.probe(audio)["format"]["duration"]) merged = self.include_gaps(merged, min_gap_length=5, total_duration=max_audio_duration) print("Transcribing non-speech:") pprint(merged) result = { 'text': "", 'segments': [], 'language': "" } languageCounter = Counter() # For each time segment, run whisper for segment in merged: segment_start = segment['start'] segment_end = segment['end'] segment_gap = segment.get('gap', False) segment_duration = segment_end - segment_start if segment_duration < MIN_SEGMENT_DURATION: continue; segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration)) print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ", segment_duration, "gap: ", segment_gap) if segment_gap: # TODO: Use different parameters for these segments, as they are less likely to contain speech segment_result = whisperCallable(segment_audio) else: 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 include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float): result = [] last_end_time = 0 for segment in segments: segment_start = float(segment['start']) segment_end = float(segment['end']) if (last_end_time != segment_start): delta = segment_start - last_end_time if (min_gap_length is None or delta >= min_gap_length): result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } ) last_end_time = segment_end result.append(segment) # Also include total duration if specified if (total_duration is not None and last_end_time < total_duration): delta = total_duration - segment_start if (min_gap_length is None or delta >= min_gap_length): result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } ) 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_merge_gap: float, max_merge_size: float): if max_merge_gap 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'] current_entry_size = current_entry['end'] - current_entry['start'] if distance <= max_merge_gap and (max_merge_size is None or current_entry_size <= max_merge_size): # 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, segment_padding_left=SEGMENT_PADDING_LEFT, segment_padding_right=SEGMENT_PADDING_RIGHT, max_silent_period=MAX_SILENT_PERIOD, max_merge_size=MAX_MERGE_SIZE, transcribe_non_speech: bool = False, copy = None): super().__init__(segment_padding_left=segment_padding_left, segment_padding_right=segment_padding_right, max_silent_period=max_silent_period, max_merge_size=max_merge_size, transcribe_non_speech=transcribe_non_speech) if copy: self.model = copy.model self.get_speech_timestamps = copy.get_speech_timestamps else: 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