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import torch # Add this line
import gradio as gr
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, pipeline, AutoTokenizer
import numpy as np
import librosa
# Load the models and processors
asr_model = Wav2Vec2ForCTC.from_pretrained("Akashpb13/Hausa_xlsr")
asr_processor = Wav2Vec2Processor.from_pretrained("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_input):
# Load the audio file as a floating point time series
audio_data, sample_rate = librosa.load(audio_input, sr=16000)
# Prepare the input dictionary
input_dict = asr_processor(audio_data, sampling_rate=sample_rate, return_tensors="pt", padding=True)
# Use the ASR model to get the logits
logits = asr_model(input_dict.input_values.to("cpu")).logits
# Get the predicted IDs
pred_ids = torch.argmax(logits, dim=-1)[0]
# Decode the predicted IDs to get the transcription
transcription = asr_processor.decode(pred_ids)
print(f"Transcription: {transcription}") # Print the 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
# 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'])
print(f"Translated text string: {translated_text_str}") # Print the translated text string
else:
print("The translated text does not contain 'generated_token_ids'")
return
# Use the text-to-speech pipeline to synthesize the translated text
synthesised_speech = tts(translated_text_str)
# 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()
# 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(type="filepath"), # 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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