Upload app.py

app.py

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+import streamlit as st
+import speech_recognition as sr
+from pocketsphinx import pocketsphinx, Jsgf, FsgModel
+import requests
+import os
+
+
+st.title("Speech to text recognition")
+
+# st.markdown("## Here we use pocketsphinx model for automatic speech recognition")
+
+audio = st.file_uploader(label = "Upload your audio file here in .wav format")
+
+
+# audio_file = '/Users/kapilgupta/Downloads/audio/videoplayback.wav'
+text_filename = "./subfolder/text_file"
+
+
+language_model = './language-model.lm.bin'
+acoustic_model = './acoustic-model'
+pronunciation_dict = './pronounciation-dictionary.dict'
+
+@st.cache
+def model(audio, text_filename):
+    framerate = 100
+    config = pocketsphinx.Config()
+    config.set_string('-hmm', acoustic_model)
+    config.set_string('-lm', language_model)
+    config.set_string('-dict', pronunciation_dict)
+    decoder = pocketsphinx.Decoder(config)
+
+    def recognize_sphinx(audio, show_all=True):
+        decoder.start_utt()
+        decoder.process_raw(audio.get_raw_data(), False, True)
+        decoder.end_utt()
+        hypothesis = decoder.hyp()
+        return decoder, hypothesis.hypstr
+
+    # Create a Recognizer instance
+    r = sr.Recognizer()
+
+    # Set the recognize_sphinx() function as the speech recognition method
+    r.recognize_sphinx = recognize_sphinx
+
+    with sr.AudioFile(audio) as source:
+        audio = r.record(source)
+        sample_rate = audio.sample_rate
+        decoder, recognized_text = r.recognize_sphinx(audio, show_all=True)
+
+
+    with open(text_filename, 'w') as text_file:
+        for seg in decoder.seg():
+            segment_info = (seg.word, seg.start_frame/sample_rate, seg.end_frame/sample_rate)
+            text_file.write(str(segment_info) + "\n")
+    return recognized_text
+
+
+if audio is not None:
+    with st.spinner("code is at Working! "):
+        segment_info = model(audio, text_filename)
+        st.write(segment_info)
+    st.balloons()
+else:
+    st.write("Upload an audio")