from abc import ABC, abstractmethod from collections import Counter, deque import os import time from typing import Any, Deque, Iterator, List, Dict from pprint import pprint from src.hooks.progressListener import ProgressListener from src.hooks.subTaskProgressListener import SubTaskProgressListener from src.hooks.whisperProgressHook import create_progress_listener_handle from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache from src.segments import merge_timestamps from src.whisper.abstractWhisperContainer import AbstractWhisperCallback # 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 src.utils import format_timestamp from enum import Enum class NonSpeechStrategy(Enum): """ Ignore non-speech frames segments. """ SKIP = 1 """ Just treat non-speech segments as speech. """ CREATE_SEGMENT = 2 """ Expand speech segments into subsequent non-speech segments. """ EXPAND_SEGMENT = 3 # Defaults for Silero SPEECH_TRESHOLD = 0.3 # Minimum size of segments to process MIN_SEGMENT_DURATION = 1 # The maximum time for texts from old segments to be used in the next segment MAX_PROMPT_WINDOW = 0 # seconds (0 = disabled) PROMPT_NO_SPEECH_PROB = 0.1 # Do not pass the text from segments with a no speech probability higher than this VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio class TranscriptionConfig(ABC): def __init__(self, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1): self.non_speech_strategy = non_speech_strategy 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.max_prompt_window = max_prompt_window self.initial_segment_index = initial_segment_index class PeriodicTranscriptionConfig(TranscriptionConfig): def __init__(self, periodic_duration: float, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1): super().__init__(non_speech_strategy, segment_padding_left, segment_padding_right, max_silent_period, max_merge_size, max_prompt_window, initial_segment_index) self.periodic_duration = periodic_duration class AbstractTranscription(ABC): def __init__(self, sampling_rate: int = 16000): self.sampling_rate = sampling_rate def get_audio_segment(self, str, start_time: str = None, duration: str = None): return load_audio(str, self.sampling_rate, start_time, duration) def is_transcribe_timestamps_fast(self): """ Determine if get_transcribe_timestamps is fast enough to not need parallelization. """ return False @abstractmethod def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float): """ Get the start and end timestamps of the sections that should be transcribed by this VAD method. Parameters ---------- audio: str The audio file. config: TranscriptionConfig The transcription configuration. Returns ------- A list of start and end timestamps, in fractional seconds. """ return def get_merged_timestamps(self, timestamps: List[Dict[str, Any]], config: TranscriptionConfig, total_duration: float): """ Get the start and end timestamps of the sections that should be transcribed by this VAD method, after merging the given segments using the specified configuration. Parameters ---------- audio: str The audio file. config: TranscriptionConfig The transcription configuration. Returns ------- A list of start and end timestamps, in fractional seconds. """ merged = merge_timestamps(timestamps, config.max_silent_period, config.max_merge_size, config.segment_padding_left, config.segment_padding_right) if config.non_speech_strategy != NonSpeechStrategy.SKIP: # Expand segments to include the gaps between them if (config.non_speech_strategy == NonSpeechStrategy.CREATE_SEGMENT): # When we have a prompt window, we create speech segments betwen each segment if we exceed the merge size merged = self.fill_gaps(merged, total_duration=total_duration, max_expand_size=config.max_merge_size) elif config.non_speech_strategy == NonSpeechStrategy.EXPAND_SEGMENT: # With no prompt window, it is better to just expand the segments (this effectively passes the prompt to the next segment) merged = self.expand_gaps(merged, total_duration=total_duration) else: raise Exception("Unknown non-speech strategy: " + str(config.non_speech_strategy)) print("Transcribing non-speech:") pprint(merged) return merged def transcribe(self, audio: str, whisperCallable: AbstractWhisperCallback, config: TranscriptionConfig, progressListener: ProgressListener = None): """ Transcribe the given audo file. Parameters ---------- audio: str The audio file. whisperCallable: WhisperCallback A callback object to call to transcribe each segment. Returns ------- A list of start and end timestamps, in fractional seconds. """ try: max_audio_duration = self.get_audio_duration(audio, config) timestamp_segments = self.get_transcribe_timestamps(audio, config, 0, max_audio_duration) # Get speech timestamps from full audio file merged = self.get_merged_timestamps(timestamp_segments, config, max_audio_duration) # A deque of transcribed segments that is passed to the next segment as a prompt prompt_window = deque() print("Processing timestamps:") pprint(merged) result = { 'text': "", 'segments': [], 'language': "" } languageCounter = Counter() detected_language = None segment_index = config.initial_segment_index # Calculate progress progress_start_offset = merged[0]['start'] if len(merged) > 0 else 0 progress_total_duration = sum([segment['end'] - segment['start'] for segment in merged]) sub_task_total = 1/len(merged) # For each time segment, run whisper for idx, segment in enumerate(merged): segment_index += 1 segment_start = segment['start'] segment_end = segment['end'] segment_expand_amount = segment.get('expand_amount', 0) segment_gap = segment.get('gap', False) segment_duration = segment_end - segment_start if segment_duration < MIN_SEGMENT_DURATION: continue # Audio to run on Whisper segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration)) # Previous segments to use as a prompt segment_prompt = ' '.join([segment['text'] for segment in prompt_window]) if len(prompt_window) > 0 else None # Detected language detected_language = languageCounter.most_common(1)[0][0] if len(languageCounter) > 0 else None print(f"Running whisper {idx}: from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ", segment_duration, "expanded: ", segment_expand_amount, ", prompt: ", segment_prompt, ", detected language: ", detected_language) perf_start_time = time.perf_counter() scaled_progress_listener = SubTaskProgressListener(progressListener, base_task_total=progressListener.sub_task_total if isinstance(progressListener, SubTaskProgressListener) else progress_total_duration, sub_task_start=idx*(1/len(merged)), sub_task_total=1/len(merged)) segment_result = whisperCallable.invoke(segment_audio, segment_index, segment_prompt, detected_language, progress_listener=scaled_progress_listener) perf_end_time = time.perf_counter() print("\tWhisper took {} seconds".format(perf_end_time - perf_start_time)) adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration) # Propagate expand amount to the segments if (segment_expand_amount > 0): segment_without_expansion = segment_duration - segment_expand_amount for adjusted_segment in adjusted_segments: adjusted_segment_end = adjusted_segment['end'] # Add expand amount if the segment got expanded if (adjusted_segment_end > segment_without_expansion): adjusted_segment["expand_amount"] = adjusted_segment_end - segment_without_expansion # Append to output result['text'] += segment_result['text'] result['segments'].extend(adjusted_segments) # Increment detected language if not segment_gap: languageCounter[segment_result['language']] += 1 # Update prompt window self.__update_prompt_window(prompt_window, adjusted_segments, segment_end, segment_gap, config) result['language'] = detected_language if detected_language is not None else segment_result['language'] finally: # Notify progress listener that we are done if progressListener is not None: progressListener.on_finished() return result def get_audio_duration(self, audio: str, config: TranscriptionConfig): return get_audio_duration(audio) def __update_prompt_window(self, prompt_window: Deque, adjusted_segments: List, segment_end: float, segment_gap: bool, config: TranscriptionConfig): if (config.max_prompt_window is not None and config.max_prompt_window > 0): # Add segments to the current prompt window (unless it is a speech gap) if not segment_gap: for segment in adjusted_segments: if segment.get('no_speech_prob', 0) <= PROMPT_NO_SPEECH_PROB: prompt_window.append(segment) while (len(prompt_window) > 0): first_end_time = prompt_window[0].get('end', 0) # Time expanded in the segments should be discounted from the prompt window first_expand_time = prompt_window[0].get('expand_amount', 0) if (first_end_time - first_expand_time < segment_end - config.max_prompt_window): prompt_window.popleft() else: break 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 # Expand the end time of each segment to the start of the next segment def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float): result = [] if len(segments) == 0: return result # Add gap at the beginning if needed if (segments[0]['start'] > 0): result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } ) for i in range(len(segments) - 1): current_segment = segments[i] next_segment = segments[i + 1] delta = next_segment['start'] - current_segment['end'] # Expand if the gap actually exists if (delta >= 0): current_segment = current_segment.copy() current_segment['expand_amount'] = delta current_segment['end'] = next_segment['start'] result.append(current_segment) # Add last segment last_segment = segments[-1] result.append(last_segment) # Also include total duration if specified if (total_duration is not None): last_segment = result[-1] if (last_segment['end'] < total_duration): last_segment = last_segment.copy() last_segment['end'] = total_duration result[-1] = last_segment return result def fill_gaps(self, segments: List[Dict[str, Any]], total_duration: float, max_expand_size: float = None): result = [] if len(segments) == 0: return result # Add gap at the beginning if needed if (segments[0]['start'] > 0): result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } ) for i in range(len(segments) - 1): expanded = False current_segment = segments[i] next_segment = segments[i + 1] delta = next_segment['start'] - current_segment['end'] if (max_expand_size is not None and delta <= max_expand_size): # Just expand the current segment current_segment = current_segment.copy() current_segment['expand_amount'] = delta current_segment['end'] = next_segment['start'] expanded = True result.append(current_segment) # Add a gap to the next segment if needed if (delta >= 0 and not expanded): result.append({ 'start': current_segment['end'], 'end': next_segment['start'], 'gap': True } ) # Add last segment last_segment = segments[-1] result.append(last_segment) # Also include total duration if specified if (total_duration is not None): last_segment = result[-1] delta = total_duration - last_segment['end'] if (delta > 0): if (max_expand_size is not None and delta <= max_expand_size): # Expand the last segment last_segment = last_segment.copy() last_segment['expand_amount'] = delta last_segment['end'] = total_duration result[-1] = last_segment else: result.append({ 'start': last_segment['end'], 'end': total_duration, 'gap': True } ) return result def adjust_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 # Handle words if ('words' in new_segment): for word in new_segment['words']: # Adjust start and end word['start'] = word['start'] + adjust_seconds word['end'] = word['end'] + adjust_seconds result.append(new_segment) 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, sampling_rate: int = 16000, cache: ModelCache = None): super().__init__(sampling_rate=sampling_rate) self.model = None self.cache = cache self._initialize_model() def _initialize_model(self): if (self.cache is not None): model_key = "VadSileroTranscription" self.model, self.get_speech_timestamps = self.cache.get(model_key, self._create_model) print("Loaded Silerio model from cache.") else: self.model, self.get_speech_timestamps = self._create_model() print("Created Silerio model") def _create_model(self): repo_owner = "snakers4" repo_name = "silero-vad" ref = "master" try: model, utils = torch.hub.load(repo_or_dir=f'{repo_owner}/{repo_name}', model='silero_vad') except Exception as e: hub_dir = torch.hub.get_dir() owner_name_branch = '_'.join([repo_owner, repo_name, ref]) repo_dir = os.path.join(hub_dir, owner_name_branch) if os.path.exists(repo_dir): print(f"vad.py: torch.hub.load({repo_owner}/{repo_name}) Exception: {str(e)}, Using cache found in {repo_dir}\n") model, utils = torch.hub.load(repo_or_dir=repo_dir, model='silero_vad', source="local") else: raise # Silero does not benefit from multi-threading torch.set_num_threads(1) # JIT (get_speech_timestamps, _, _, _, _) = utils return model, get_speech_timestamps def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float): result = [] print("Getting timestamps from audio file: {}, start: {}, duration: {}".format(audio, start_time, end_time)) perf_start_time = time.perf_counter() # Divide procesisng of audio into chunks chunk_start = start_time while (chunk_start < end_time): chunk_duration = min(end_time - chunk_start, VAD_MAX_PROCESSING_CHUNK) print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration))) wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration)) 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) adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration) #pprint(adjusted) result.extend(adjusted) chunk_start += chunk_duration perf_end_time = time.perf_counter() print("VAD processing took {} seconds".format(perf_end_time - perf_start_time)) return result def __getstate__(self): # We only need the sampling rate return { 'sampling_rate': self.sampling_rate } def __setstate__(self, state): self.sampling_rate = state['sampling_rate'] self.model = None # Use the global cache self.cache = GLOBAL_MODEL_CACHE self._initialize_model() # A very simple VAD that just marks every N seconds as speech class VadPeriodicTranscription(AbstractTranscription): def __init__(self, sampling_rate: int = 16000): super().__init__(sampling_rate=sampling_rate) def is_transcribe_timestamps_fast(self): # This is a very fast VAD - no need to parallelize it return True def get_transcribe_timestamps(self, audio: str, config: PeriodicTranscriptionConfig, start_time: float, end_time: float): result = [] # Generate a timestamp every N seconds start_timestamp = start_time while (start_timestamp < end_time): end_timestamp = min(start_timestamp + config.periodic_duration, end_time) 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 get_audio_duration(file: str): return float(ffmpeg.probe(file)["format"]["duration"]) 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