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import gradio as gr | |
import requests | |
from gtts import gTTS # Import gTTS for Text-to-Speech | |
import tempfile | |
# Function to convert text to speech and transcribe | |
def text_to_speech_transcribe(text): | |
# Convert text to speech | |
tts = gTTS(text, lang='en') | |
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_audio: | |
audio_path = tmp_audio.name | |
tts.save(audio_path) | |
# Read audio file in binary mode | |
with open(audio_path, "rb") as audio_file: | |
audio_data = audio_file.read() | |
# 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 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", # Replace with your Groq API key | |
} | |
# Prepare the files and data for the request | |
files = { | |
'file': ('audio.wav', audio_data, '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=text_to_speech_transcribe, | |
inputs="text", # Input text to be converted to speech | |
outputs="text", | |
title="Text to Speech and Transcription", | |
description="Enter text to convert it to audio, then transcribe it with the Groq API." | |
) | |
iface.launch() | |