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| import gradio as gr | |
| import requests | |
| import numpy as np | |
| import io | |
| import wave | |
| # Function to send audio to Groq API and get transcription | |
| def transcribe(audio_data): | |
| # Convert the NumPy audio array to bytes | |
| audio_bytes = io.BytesIO() | |
| # Convert NumPy array to WAV format (use appropriate rate, channels, etc.) | |
| with wave.open(audio_bytes, "wb") as wf: | |
| wf.setnchannels(1) # Mono channel | |
| wf.setsampwidth(2) # 16-bit audio | |
| wf.setframerate(16000) # Assuming 16kHz sample rate | |
| wf.writeframes(audio_data.tobytes()) | |
| audio_bytes.seek(0) # Rewind to the beginning | |
| # Groq API endpoint for audio transcription | |
| groq_api_endpoint = "https://api.groq.com/openai/v1/audio/transcriptions" | |
| # Replace 'YOUR_GROQ_API_KEY' with your actual Groq API key | |
| headers = { | |
| "Authorization": "Bearer YOUR_GROQ_API_KEY", | |
| } | |
| # Prepare the files and data for the request | |
| files = { | |
| 'file': ('audio.wav', audio_bytes, 'audio/wav'), | |
| } | |
| data = { | |
| 'model': 'whisper-large-v3-turbo', # Specify the model to use | |
| 'response_format': 'json', # Desired response format | |
| 'language': 'en', # Language of the audio | |
| } | |
| # Send audio to Groq API | |
| response = requests.post(groq_api_endpoint, headers=headers, files=files, data=data) | |
| # Parse response | |
| if response.status_code == 200: | |
| result = response.json() | |
| return result.get("text", "No transcription available.") | |
| else: | |
| return f"Error: {response.status_code}, {response.text}" | |
| # Gradio interface | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=gr.Audio(source="microphone", type="numpy"), # Changed to numpy | |
| outputs="text", | |
| title="Voice to Text Converter", | |
| description="Record your voice, and it will be transcribed into text using Groq API." | |
| ) | |
| iface.launch() | |