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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
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