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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-large-xlsr-53-spanish") |
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sampling_rate = feature_extractor.sampling_rate |
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asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-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) |
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sentiment={dic["label"]: dic["score"] for dic in sentiment} |
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pipe2 = pipeline("text-classification", model = "hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021") |
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sexism_detection = pipe2(transcribed_text) |
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sexism_detection={dic["label"]: dic["score"] for dic in sexism_detection} |
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pipe3 = pipeline("text-classification", model = "hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021") |
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harassment_detection = pipe3(transcribed_text) |
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harassment_detection={dic["label"]: dic["score"] for dic in harassment_detection} |
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return harassment_detection |
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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.Label(num_top_classes=2)], |
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examples=[["audio1.wav"], ["audio2.wav"]], |
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title="Spanish-Audio-Transcription-based-Harassment-Detection", |
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description="This is a Gradio demo for Sentiment Analysis of Transcribed Spanish Audio", |
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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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