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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-xls-r-1b-spanish")
sampling_rate = feature_extractor.sampling_rate
asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-xls-r-1b-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)[0]["label"]
return f"Detected Sentiment: {sentiment}"
description = """ This is a Gradio demo for Sentiment Analysis of Transcribed Spanish Audio. First, we do Speech to Text, and then we perform sentiment analysis on the obtained transcription of the input audio.
**NOTE regarding predicted labels : NEG --> NEGATIVE, NEU--> NEUTRAL, POS--> POSITIVE**
Pre-trained model used for Spanish ASR: [jonatasgrosman/wav2vec2-xls-r-1b-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-spanish)
Pre-trained model used for Sentiment Analysis of transcribed audio: [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis)
"""
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.Textbox(label="Predicción")],
examples=[["audio_test.wav"], ["sample_audio.wav"], ["test2.wav"]],
title="Sentiment Analysis of Spanish Transcribed Audio",
description=description,
layout="horizontal",
theme="huggingface",
).launch(enable_queue=True, cache_examples=True)
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