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import gradio
import torch
import numpy as np
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")
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)
tts_tokenizer = AutoTokenizer.from_pretrained("Baghdad99/english_voice_tts")
tts_model = AutoModelForTextToWaveform.from_pretrained("Baghdad99/english_voice_tts")
# Define the translation and synthesis functions
def translate(audio_signal):
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])
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]
return translated_text
def synthesise(translated_text):
inputs = tts_tokenizer(translated_text, return_tensors='pt')
audio = tts_model.generate(inputs['input_ids'])
return audio
def translate_speech(audio):
translated_text = translate(audio)
synthesised_speech = synthesise(translated_text)
synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
return 16000, synthesised_speech
# Define the Gradio interface
iface = gradio.Interface(fn=translate_speech, inputs=gradio.inputs.Audio(source="microphone", type="numpy"), outputs="audio")
iface.launch()
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