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import gradio as gr
import librosa
from transformers import AutoFeatureExtractor, AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
def load_and_fix_data(input_file, model_sampling_rate):
speech, sample_rate = librosa.load(input_file)
if len(speech.shape) > 1:
speech = speech[:, 0] + speech[:, 1]
if sample_rate != model_sampling_rate:
speech = librosa.resample(speech, sample_rate, model_sampling_rate)
return speech
feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
sampling_rate = feature_extractor.sampling_rate
asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
model = AutoModelForSeq2SeqLM.from_pretrained('hackathon-pln-es/t5-small-spanish-nahuatl')
tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/t5-small-spanish-nahuatl')
new_line = '\n'
def predict_and_ctc_lm_decode(input_file):
speech = load_and_fix_data(input_file, sampling_rate)
transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1)
transcribed_text = transcribed_text["text"]
input_ids = tokenizer('translate Spanish to Nahuatl: ' + transcribed_text, return_tensors='pt').input_ids
outputs = model.generate(input_ids, max_length=512)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
return f"Spanish Audio Transcription: {transcribed_text} {new_line} Nahuatl Translation :{outputs}"
gr.Interface(
predict_and_ctc_lm_decode,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")
],
outputs=[gr.outputs.Textbox()],
examples=[["audio1.wav"], ["travel.wav"]],
title="Spanish-Audio-Transcriptions-to-Quechua-Translation",
description = "This is a Gradio demo of Spanish Audio Transcriptions to Nahuatl Translation. To use this, simply provide an audio input (audio recording or via microphone), which will subsequently be transcribed and translated to Nahuatl language.",
#article="<p><center><img src='........e'></center></p>",
layout="horizontal",
theme="huggingface",
).launch(enable_queue=True, cache_examples=True)
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