app.py

import gradio as gr
import librosa
from transformers import AutoFeatureExtractor, 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")


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)["text"]
    pipe1 = pipeline("sentiment-analysis", model = "finiteautomata/beto-sentiment-analysis")
    sentiment = pipe1(transcribed_text)
    sentiment={dic["label"]: dic["score"] for dic in sentiment}
    pipe2 = pipeline("text-classification", model = "hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021")
    sexism_detection = pipe2(transcribed_text)
    sexism_detection={dic["label"]: dic["score"] for dic in sexism_detection}
    #sexism_detection = np.where(sexism_detection['label']== 0, 'No Sexista', 'Sexista')
    pipe3 = pipeline("text-classification", model = "hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021")
    harassment_detection = pipe3(transcribed_text)
    harassment_detection={dic["label"]: dic["score"] for dic in harassment_detection}
    #harassment_detection = np.where(harassment_detection['label']== 0, 'No Harassment', 'Harassment')
    return sentiment
#sexism_detection, harassment_detection

gr.Interface(
    predict_and_ctc_lm_decode,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")
    ],
    #outputs=[gr.outputs.Label(num_top_classes=2),gr.outputs.Label(num_top_classes=2), gr.outputs.Label(num_top_classes=2)],
    outputs=[gr.outputs.Label(num_top_classes=2)],
    examples=[["respiracion_happiness.wav"]],
    title="Sentiment Analysis of Spanish Transcribed Audio",
    description="This is a Gradio demo for Sentiment Analysis of Transcribed Spanish Audio",
    layout="horizontal",
    theme="huggingface",
).launch(enable_queue=True, cache_examples=True)