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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,139 @@ | ||
| import datetime | ||
| import logging | ||
| import threading | ||
| from typing import Optional | ||
|
|
||
| import numpy as np | ||
| import sounddevice | ||
| import whisper | ||
| from PyQt6.QtCore import QObject, pyqtSignal | ||
| from sounddevice import PortAudioError | ||
|
|
||
| from buzz import transformers_whisper | ||
| from buzz.model_loader import ModelType | ||
| from buzz.transcriber import TranscriptionOptions, WhisperCpp, whisper_cpp_params | ||
| from buzz.transformers_whisper import TransformersWhisper | ||
|
|
||
|
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| class RecordingTranscriber(QObject): | ||
| transcription = pyqtSignal(str) | ||
| finished = pyqtSignal() | ||
| error = pyqtSignal(str) | ||
| is_running = False | ||
| MAX_QUEUE_SIZE = 10 | ||
|
|
||
| def __init__(self, transcription_options: TranscriptionOptions, | ||
| input_device_index: Optional[int], sample_rate: int, model_path: str, | ||
| parent: Optional[QObject] = None) -> None: | ||
| super().__init__(parent) | ||
| self.transcription_options = transcription_options | ||
| self.current_stream = None | ||
| self.input_device_index = input_device_index | ||
| self.sample_rate = sample_rate | ||
| self.model_path = model_path | ||
| self.n_batch_samples = 5 * self.sample_rate # every 5 seconds | ||
| # pause queueing if more than 3 batches behind | ||
| self.max_queue_size = 3 * self.n_batch_samples | ||
| self.queue = np.ndarray([], dtype=np.float32) | ||
| self.mutex = threading.Lock() | ||
|
|
||
| def start(self): | ||
| model_path = self.model_path | ||
|
|
||
| if self.transcription_options.model.model_type == ModelType.WHISPER: | ||
| model = whisper.load_model(model_path) | ||
| elif self.transcription_options.model.model_type == ModelType.WHISPER_CPP: | ||
| model = WhisperCpp(model_path) | ||
| else: # ModelType.HUGGING_FACE | ||
| model = transformers_whisper.load_model(model_path) | ||
|
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||
| initial_prompt = self.transcription_options.initial_prompt | ||
|
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| logging.debug('Recording, transcription options = %s, model path = %s, sample rate = %s, device = %s', | ||
| self.transcription_options, model_path, self.sample_rate, self.input_device_index) | ||
|
|
||
| self.is_running = True | ||
| try: | ||
| with sounddevice.InputStream(samplerate=self.sample_rate, | ||
| device=self.input_device_index, dtype="float32", | ||
| channels=1, callback=self.stream_callback): | ||
| while self.is_running: | ||
| self.mutex.acquire() | ||
| if self.queue.size >= self.n_batch_samples: | ||
| samples = self.queue[:self.n_batch_samples] | ||
| self.queue = self.queue[self.n_batch_samples:] | ||
| self.mutex.release() | ||
|
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||
| logging.debug('Processing next frame, sample size = %s, queue size = %s, amplitude = %s', | ||
| samples.size, self.queue.size, self.amplitude(samples)) | ||
| time_started = datetime.datetime.now() | ||
|
|
||
| if self.transcription_options.model.model_type == ModelType.WHISPER: | ||
| assert isinstance(model, whisper.Whisper) | ||
| result = model.transcribe( | ||
| audio=samples, language=self.transcription_options.language, | ||
| task=self.transcription_options.task.value, | ||
| initial_prompt=initial_prompt, | ||
| temperature=self.transcription_options.temperature) | ||
| elif self.transcription_options.model.model_type == ModelType.WHISPER_CPP: | ||
| assert isinstance(model, WhisperCpp) | ||
| result = model.transcribe( | ||
| audio=samples, | ||
| params=whisper_cpp_params( | ||
| language=self.transcription_options.language | ||
| if self.transcription_options.language is not None else 'en', | ||
| task=self.transcription_options.task.value, word_level_timings=False)) | ||
| else: | ||
| assert isinstance(model, TransformersWhisper) | ||
| result = model.transcribe(audio=samples, | ||
| language=self.transcription_options.language | ||
| if self.transcription_options.language is not None else 'en', | ||
| task=self.transcription_options.task.value) | ||
|
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||
| next_text: str = result.get('text') | ||
|
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| # Update initial prompt between successive recording chunks | ||
| initial_prompt += next_text | ||
|
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| logging.debug('Received next result, length = %s, time taken = %s', | ||
| len(next_text), datetime.datetime.now() - time_started) | ||
| self.transcription.emit(next_text) | ||
| else: | ||
| self.mutex.release() | ||
| except PortAudioError as exc: | ||
| self.error.emit(str(exc)) | ||
| logging.exception('') | ||
| return | ||
|
|
||
| self.finished.emit() | ||
|
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||
| @staticmethod | ||
| def get_device_sample_rate(device_id: Optional[int]) -> int: | ||
| """Returns the sample rate to be used for recording. It uses the default sample rate | ||
| provided by Whisper if the microphone supports it, or else it uses the device's default | ||
| sample rate. | ||
| """ | ||
| whisper_sample_rate = whisper.audio.SAMPLE_RATE | ||
| try: | ||
| sounddevice.check_input_settings( | ||
| device=device_id, samplerate=whisper_sample_rate) | ||
| return whisper_sample_rate | ||
| except PortAudioError: | ||
| device_info = sounddevice.query_devices(device=device_id) | ||
| if isinstance(device_info, dict): | ||
| return int(device_info.get('default_samplerate', whisper_sample_rate)) | ||
| return whisper_sample_rate | ||
|
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||
| def stream_callback(self, in_data: np.ndarray, frame_count, time_info, status): | ||
| # Try to enqueue the next block. If the queue is already full, drop the block. | ||
| chunk: np.ndarray = in_data.ravel() | ||
| with self.mutex: | ||
| if self.queue.size < self.max_queue_size: | ||
| self.queue = np.append(self.queue, chunk) | ||
|
|
||
| @staticmethod | ||
| def amplitude(arr: np.ndarray): | ||
| return (abs(max(arr)) + abs(min(arr))) / 2 | ||
|
|
||
| def stop_recording(self): | ||
| self.is_running = False |
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