app.py

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()