added application file

app.py

@@ -0,0 +1,57 @@
+# -*- coding: utf-8 -*-
+
+import crepe
+import spacy
+import librosa
+import gradio as gr
+import pandas as pd
+from transformers import pipeline
+
+asr = pipeline('automatic-speech-recognition', model='facebook/wav2vec2-large-960h-lv60-self')
+emo = pipeline('sentiment-analysis', model='arpanghoshal/EmoRoBERTa')
+lang_model = spacy.load("en_core_web_sm")
+
+def transcribe_and_describe(audio):
+
+  audio, sr = librosa.load(audio, sr=16000)
+
+  text = asr(audio)['text']
+
+  doc = lang_model(text)
+  filler_words = [token.text for token in doc if token.pos_ == 'INTJ']
+  filler_word_pr =  len(filler_words) / len(doc)
+
+  flatness = pd.DataFrame(librosa.feature.spectral_flatness(y=audio).T).describe().T
+  loudness = pd.DataFrame(librosa.feature.rms(audio).T).describe().T
+  time, frequency, confidence, activation = crepe.predict(audio, sr)
+  frequency = pd.DataFrame(frequency.T).describe().T
+
+  mean_spectral_flatness = flatness.loc[0, 'mean'] 
+  spectral_flatness_std = flatness.loc[0, 'std'] 
+  mean_pitch = frequency.loc[0, 'mean'] 
+  pitch_std = frequency.loc[0, 'std'] 
+  mean_volume = loudness.loc[0, 'mean'] 
+  volume_std = loudness.loc[0, 'std'] 
+
+  words_per_minute = len(text.split(" ")) / (librosa.get_duration(audio, sr) / 60)
+
+  emotion = emo(text)[0]['label']
+
+  return (text, filler_word_pr, words_per_minute, mean_pitch, pitch_std, mean_volume, volume_std, mean_spectral_flatness, spectral_flatness_std,  emotion)
+
+gr.Interface(
+    fn=transcribe_and_describe, 
+    inputs=gr.Audio(source="microphone", type="filepath"), 
+    outputs=[
+        gr.Text(label="Transcription"), 
+        gr.Text(label="Rate of Speech (WPM)"), 
+        gr.Text(label="Filler Word Percent"),
+        gr.Text(label="Mean Pitch (Hz)"), 
+        gr.Text(label="Pitch Variation (Hz)"), 
+        gr.Text(label="Mean Volume (W)"),
+        gr.Text(label="Volume Variation (W)"),
+        gr.Text(label="Mean Spectral Flatness (dB)"),
+        gr.Text(label="Spectral Flatness Variation (dB)"),
+        gr.Text(label="Emotion")
+        ]
+        ).launch()