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Upload app.py

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app.py ADDED
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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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+
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+
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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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+
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+
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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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+
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+ asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
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+
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+
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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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+ #sexism_detection = np.where(sexism_detection['label']== 0, 'No Sexista', 'Sexista')
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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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+ #harassment_detection = np.where(harassment_detection['label']== 0, 'No Harassment', 'Harassment')
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+ return sentiment
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+ #sexism_detection, harassment_detection
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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.Label(num_top_classes=2),gr.outputs.Label(num_top_classes=2), gr.outputs.Label(num_top_classes=2)],
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+ outputs=[gr.outputs.Label(num_top_classes=2)],
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+ examples=[["respiracion_happiness.wav"]],
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+ title="Sentiment Analysis of Spanish Transcribed Audio",
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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)