app.py

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} The corresponding Nahuatl Translation is :{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"]],
    title="Spanish-Audio-Transcriptions-to-Nahuatl-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)