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import gradio as gr
from transformers import pipeline
import numpy as np
# Load the pipeline for speech recognition and translation
pipe = pipeline(
"automatic-speech-recognition",
model="Baghdad99/saad-speech-recognition-hausa-audio-to-text",
tokenizer="Baghdad99/saad-speech-recognition-hausa-audio-to-text"
)
translator = pipeline("text2text-generation", model="Baghdad99/saad-hausa-text-to-english-text")
tts = pipeline("text-to-speech", model="Baghdad99/english_voice_tts")
def translate_speech(audio):
# Separate the sample rate and the audio data
sample_rate, audio_data = audio
# Use the speech recognition pipeline to transcribe the audio
output = pipe(audio_data)
print(f"Output: {output}") # Print the output to see what it contains
# Check if the output contains 'transcription'
if 'transcription' in output:
transcription = output["transcription"]
else:
print("The output does not contain 'transcription'")
return
# Rest of your code...
# # Define the function to translate speech
# def translate_speech(audio):
# # Separate the sample rate and the audio data
# sample_rate, audio_data = audio
# # Use the speech recognition pipeline to transcribe the audio
# transcription = pipe(audio_data)["transcription"]
# # Use the translation pipeline to translate the transcription
# translated_text = translator(transcription, return_tensors="pt", padding=True)
# # Use the text-to-speech pipeline to synthesize the translated text
# synthesised_speech = tts(translated_text, return_tensors='pt')
# # Define the max_range variable
# max_range = 32767 # You can adjust this value based on your requirements
# synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
# return 16000, synthesised_speech
# Define the Gradio interface
iface = gr.Interface(
fn=translate_speech,
inputs=gr.inputs.Audio(source="microphone", type="numpy"),
outputs=gr.outputs.Audio(type="numpy"),
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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