Delete whisper.py
Browse files- whisper.py +0 -68
whisper.py
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import os
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import tempfile
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import logging
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import requests
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from typing import Optional
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import edge_tts
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from config.config import VOICE, FALLBACK_VOICES, token
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logger = logging.getLogger(__name__)
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# Whisper model for speech to text
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API_URL = "https://api-inference.huggingface.co/models/openai/whisper-tiny"
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headers = {"Authorization": f"Bearer {token}"}
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# Voice selection handling
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async def get_valid_voice() -> str:
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available_voices = await edge_tts.list_voices()
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voice_names = [VOICE] + FALLBACK_VOICES
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available_voice_names = {v["ShortName"] for v in available_voices}
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for voice in voice_names:
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if voice in available_voice_names:
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return voice
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raise RuntimeError("No valid voice found")
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# Text-to-speech conversion using Edge TTS
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async def generate_speech(text: str) -> Optional[str]:
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if not text or not isinstance(text, str):
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raise ValueError("Invalid text input")
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voice = await get_valid_voice()
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logger.info(f"Using voice: {voice}")
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with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
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tmp_path = tmp_file.name
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communicate = edge_tts.Communicate(text, voice)
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await communicate.save(tmp_path)
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if not os.path.exists(tmp_path) or os.path.getsize(tmp_path) == 0:
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raise RuntimeError("Speech file empty or not created")
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logger.info(f"Speech generated successfully: {tmp_path}")
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return tmp_path
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# Speech-to-text using Whisper
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async def transcribe(audio_file: str) -> str:
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try:
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with open(audio_file, "rb") as f:
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data = f.read()
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response = requests.post(API_URL, headers=headers, data=data)
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result = response.json()
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if "text" in result:
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transcription = result["text"].strip()
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logger.info(f"Transcribed text: {transcription}")
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return transcription
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else:
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raise ValueError("No transcription in response")
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except Exception as e:
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logger.error(f"Transcription error: {str(e)}")
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raise RuntimeError(f"Failed to transcribe audio: {str(e)}")
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