Upload app.py

app.py

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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-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)