DrishtiSharma
commited on
Commit
•
619b35a
1
Parent(s):
aa1f998
Update app.py
Browse files
app.py
CHANGED
@@ -22,18 +22,9 @@ def predict_and_ctc_lm_decode(input_file):
|
|
22 |
speech = load_and_fix_data(input_file, sampling_rate)
|
23 |
transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1)["text"]
|
24 |
pipe1 = pipeline("sentiment-analysis", model = "finiteautomata/beto-sentiment-analysis")
|
25 |
-
sentiment = pipe1(transcribed_text)
|
26 |
-
sentiment={dic["label"]: dic["score"] for dic in sentiment}
|
27 |
-
pipe2 = pipeline("text-classification", model = "hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021")
|
28 |
-
sexism_detection = pipe2(transcribed_text)
|
29 |
-
sexism_detection={dic["label"]: dic["score"] for dic in sexism_detection}
|
30 |
-
#sexism_detection = np.where(sexism_detection['label']== 0, 'No Sexista', 'Sexista')
|
31 |
-
pipe3 = pipeline("text-classification", model = "hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021")
|
32 |
-
harassment_detection = pipe3(transcribed_text)
|
33 |
-
harassment_detection={dic["label"]: dic["score"] for dic in harassment_detection}
|
34 |
-
#harassment_detection = np.where(harassment_detection['label']== 0, 'No Harassment', 'Harassment')
|
35 |
return sentiment
|
36 |
-
|
37 |
|
38 |
gr.Interface(
|
39 |
predict_and_ctc_lm_decode,
|
@@ -41,7 +32,7 @@ gr.Interface(
|
|
41 |
gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")
|
42 |
],
|
43 |
#outputs=[gr.outputs.Label(num_top_classes=2),gr.outputs.Label(num_top_classes=2), gr.outputs.Label(num_top_classes=2)],
|
44 |
-
outputs=[gr.outputs.
|
45 |
examples=[["audio_test.wav"], ["sample_audio.wav"]],
|
46 |
title="Sentiment Analysis of Spanish Transcribed Audio",
|
47 |
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.",
|
|
|
22 |
speech = load_and_fix_data(input_file, sampling_rate)
|
23 |
transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1)["text"]
|
24 |
pipe1 = pipeline("sentiment-analysis", model = "finiteautomata/beto-sentiment-analysis")
|
25 |
+
sentiment = pipe1(transcribed_text)[0]["label"]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
26 |
return sentiment
|
27 |
+
|
28 |
|
29 |
gr.Interface(
|
30 |
predict_and_ctc_lm_decode,
|
|
|
32 |
gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")
|
33 |
],
|
34 |
#outputs=[gr.outputs.Label(num_top_classes=2),gr.outputs.Label(num_top_classes=2), gr.outputs.Label(num_top_classes=2)],
|
35 |
+
outputs=[gr.outputs.Textbox(label="Predicción")],
|
36 |
examples=[["audio_test.wav"], ["sample_audio.wav"]],
|
37 |
title="Sentiment Analysis of Spanish Transcribed Audio",
|
38 |
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.",
|