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import gradio as gr |
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import librosa |
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from transformers import AutoFeatureExtractor, pipeline |
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def load_and_fix_data(input_file, model_sampling_rate): |
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speech, sample_rate = librosa.load(input_file) |
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if len(speech.shape) > 1: |
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speech = speech[:, 0] + speech[:, 1] |
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if sample_rate != model_sampling_rate: |
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speech = librosa.resample(speech, sample_rate, model_sampling_rate) |
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return speech |
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feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-xls-r-1b-spanish") |
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sampling_rate = feature_extractor.sampling_rate |
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asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-xls-r-1b-spanish") |
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def predict_and_ctc_lm_decode(input_file): |
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speech = load_and_fix_data(input_file, sampling_rate) |
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transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1)["text"] |
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pipe1 = pipeline("sentiment-analysis", model = "finiteautomata/beto-sentiment-analysis") |
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sentiment = pipe1(transcribed_text)[0]["label"] |
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return f"Detected Sentiment: {sentiment}" |
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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. |
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**NOTE regarding predicted labels : NEG --> NEGATIVE, NEU--> NEUTRAL, POS--> POSITIVE** |
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Pre-trained model used for Spanish ASR: [jonatasgrosman/wav2vec2-xls-r-1b-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-spanish) |
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Pre-trained model used for Sentiment Analysis of transcribed audio: [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) |
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""" |
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gr.Interface( |
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predict_and_ctc_lm_decode, |
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inputs=[ |
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gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio") |
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], |
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outputs=[gr.outputs.Textbox(label="Predicción")], |
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examples=[["audio_test.wav"], ["sample_audio.wav"], ["test2.wav"]], |
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title="Sentiment Analysis of Spanish Transcribed Audio", |
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description=description, |
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layout="horizontal", |
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theme="huggingface", |
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).launch(enable_queue=True, cache_examples=True) |
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