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import gradio as gr
from transformers import pipeline, AutoTokenizer
import numpy as np
from pydub import AudioSegment
# Load the pipeline for speech recognition and translation
pipe = pipeline(
"automatic-speech-recognition",
model="Akashpb13/Hausa_xlsr",
tokenizer="Akashpb13/Hausa_xlsr"
)
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_data_tuple):
print(f"Type of audio: {type(audio_data_tuple)}, Value of audio: {audio_data_tuple}") # Debug line
# Extract the audio data from the tuple
sample_rate, audio_data = audio_data_tuple
# Print the shape and type of the audio data
print(f"Audio data type: {type(audio_data)}, Audio data shape: {audio_data.shape}")
# Use the speech recognition pipeline to transcribe the audio
output = pipe(audio_data.astype(np.float64)) # Convert to Double
print(f"Output: {output}") # Print the output to see what it contains
# Check if the output contains 'text'
if 'text' in output:
transcription = output["text"]
else:
print("The output does not contain 'text'")
return
# Print the transcription
print(f"Transcription: {transcription}")
# Use the translation pipeline to translate the transcription
translated_text = translator(transcription, return_tensors="pt")
print(f"Translated text: {translated_text}") # Print the translated text to see what it contains
# Check if the translated text contains 'generated_token_ids'
if 'generated_token_ids' in translated_text[0]:
# Decode the tokens into text
translated_text_str = translator.tokenizer.decode(translated_text[0]['generated_token_ids'])
else:
print("The translated text does not contain 'generated_token_ids'")
return
# Print the translated text string
print(f"Translated text string: {translated_text_str}")
# Use the text-to-speech pipeline to synthesize the translated text
synthesised_speech = tts(translated_text_str)
print(f"Synthesised speech: {synthesised_speech}") # Print the synthesised speech to see what it contains
# Check if the synthesised speech contains 'audio'
if 'audio' in synthesised_speech:
synthesised_speech_data = synthesised_speech['audio']
else:
print("The synthesised speech does not contain 'audio'")
return
# Flatten the audio data
synthesised_speech_data = synthesised_speech_data.flatten()
# Print the shape and type of the synthesised speech data
print(f"Synthesised speech data type: {type(synthesised_speech_data)}, Synthesised speech data shape: {synthesised_speech_data.shape}")
# Scale the audio data to the range of int16 format
synthesised_speech = (synthesised_speech_data * 32767).astype(np.int16)
return 16000, synthesised_speech
# Define the Gradio interface
iface = gr.Interface(
fn=translate_speech,
inputs=gr.inputs.Audio(source="microphone"), # Change this line
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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