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| 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) | |
| 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 |