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import gradio as gr
import requests
from IPython.display import Audio
# Define the Hugging Face Inference API URLs and headers
ASR_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/saad-speech-recognition-hausa-audio-to-text"
TTS_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/english_voice_tts"
TRANSLATION_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/saad-hausa-text-to-english-text"
headers = {"Authorization": "Bearer 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"}
# Define the function to query the Hugging Face Inference API
def query(api_url, payload):
response = requests.post(api_url, headers=headers, json=payload)
return response.json()
# Define the function to translate speech
def translate_speech(audio):
# Use the ASR pipeline to transcribe the audio
with open(audio.name, "rb") as f:
data = f.read()
response = requests.post(ASR_API_URL, headers=headers, data=data)
output = response.json()
# Check if the output contains 'text'
if 'text' in output:
transcription = output["text"]
else:
print("The output does not contain 'text'")
return
# Use the translation pipeline to translate the transcription
translated_text = query(TRANSLATION_API_URL, {"inputs": transcription})
# Use the TTS pipeline to synthesize the translated text
response = requests.post(TTS_API_URL, headers=headers, json={"inputs": translated_text})
audio_bytes = response.content
return audio_bytes
# Define the Gradio interface
iface = gr.Interface(
fn=translate_speech,
inputs=gr.inputs.Audio(source="microphone", type="file"),
outputs=gr.outputs.Audio(type="auto"),
title="Hausa to English Translation",
description="Realtime demo for Hausa to English translation using speech recognition and text-to-speech synthesis."
)
iface.launch()
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