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| import io | |
| import os | |
| import types | |
| import wave | |
| import speech_recognition as sr | |
| import whisper | |
| from pydub import AudioSegment | |
| from realtime_ai_character.audio.speech_to_text.base import SpeechToText | |
| from realtime_ai_character.logger import get_logger | |
| from realtime_ai_character.utils import Singleton | |
| DEBUG = False | |
| logger = get_logger(__name__) | |
| config = types.SimpleNamespace(**{ | |
| 'model': 'tiny', | |
| 'language': 'en', | |
| 'api_key': os.getenv("OPENAI_API_KEY"), | |
| }) | |
| class Whisper(Singleton, SpeechToText): | |
| def __init__(self, use='local'): | |
| super().__init__() | |
| if use == 'local': | |
| logger.info(f"Loading [Local Whisper] model: [{config.model}]...") | |
| whisper.load_model(config.model) | |
| self.recognizer = sr.Recognizer() | |
| self.use = use | |
| if DEBUG: | |
| self.wf = wave.open('output.wav', 'wb') | |
| self.wf.setnchannels(1) # Assuming mono audio | |
| self.wf.setsampwidth(2) # Assuming 16-bit audio | |
| self.wf.setframerate(44100) # Assuming 44100Hz sample rate | |
| def transcribe(self, audio_bytes, platform, prompt=''): | |
| logger.info("Transcribing audio...") | |
| if platform == 'web': | |
| audio = self._convert_webm_to_wav(audio_bytes) | |
| else: | |
| audio = sr.AudioData(audio_bytes, 44100, 2) | |
| if self.use == 'local': | |
| return self._transcribe(audio, prompt) | |
| elif self.use == 'api': | |
| return self._transcribe_api(audio, prompt) | |
| def _transcribe(self, audio, prompt=''): | |
| text = self.recognizer.recognize_whisper( | |
| audio, | |
| model=config.model, | |
| language=config.language, | |
| show_dict=True, | |
| initial_prompt=prompt | |
| )['text'] | |
| return text | |
| def _transcribe_api(self, audio, prompt=''): | |
| text = self.recognizer.recognize_whisper_api( | |
| audio, | |
| api_key=config.api_key, | |
| ) | |
| return text | |
| def _convert_webm_to_wav(self, webm_data): | |
| webm_audio = AudioSegment.from_file( | |
| io.BytesIO(webm_data), format="webm") | |
| wav_data = io.BytesIO() | |
| webm_audio.export(wav_data, format="wav") | |
| with sr.AudioFile(wav_data) as source: | |
| audio = self.recognizer.record(source) | |
| return audio | |