app.py

import gradio as gr
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForTextToWaveform

# Load your pretrained models
asr_model = Wav2Vec2ForCTC.from_pretrained("Baghdad99/saad-speech-recognition-hausa-audio-to-text")
asr_processor = Wav2Vec2Processor.from_pretrained("Baghdad99/saad-speech-recognition-hausa-audio-to-text")

# Load the Hausa translation model
translation_tokenizer = AutoTokenizer.from_pretrained("Baghdad99/saad-hausa-text-to-english-text")
translation_model = AutoModelForSeq2SeqLM.from_pretrained("Baghdad99/saad-hausa-text-to-english-text", from_tf=True)

# Load the Text-to-Speech model
tts_tokenizer = AutoTokenizer.from_pretrained("Baghdad99/english_voice_tts")
tts_model = AutoModelForTextToWaveform.from_pretrained("Baghdad99/english_voice_tts")

def translate_speech(speech):
    # Extract the audio signal and sample rate
    audio_signal, sample_rate = speech

    # Convert stereo to mono if necessary
    if len(audio_signal.shape) > 1:
        audio_signal = audio_signal.mean(axis=0)

    # Transcribe the speech to text
    inputs = asr_processor(audio_signal, return_tensors="pt", padding=True)
    logits = asr_model(inputs.input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = asr_processor.decode(predicted_ids[0])

    # Translate the text
    translated = translation_model.generate(**translation_tokenizer(transcription, return_tensors="pt", padding=True))
    translated_text = [translation_tokenizer.decode(t, skip_special_tokens=True) for t in translated]

    # Convert the translated text to speech
    inputs = tts_tokenizer(translated_text, return_tensors='pt')
    audio = tts_model.generate(inputs['input_ids'])

    return audio


# Define the Gradio interface
iface = gr.Interface(fn=translate_speech, inputs=gr.inputs.Audio(source="microphone"), outputs="audio")
iface.launch()