Merge pull request #109 from lifebottle/pnvnd-patch-1

Create wav_to_txt.py

wav_to_txt.py

@@ -0,0 +1,61 @@
+# Usage with IDLE:
+# path = "path/to/audio.wav"
+# print("\nFull text:", get_large_audio_transcription_on_silence(path))
+
+# importing libraries 
+import speech_recognition as sr 
+import os 
+from pydub import AudioSegment
+from pydub.silence import split_on_silence
+
+# create a speech recognition object
+r = sr.Recognizer()
+
+# a function to recognize speech in the audio file
+# so that we don't repeat ourselves in in other functions
+def transcribe_audio(path):
+    # use the audio file as the audio source
+    with sr.AudioFile(path) as source:
+        audio_listened = r.record(source)
+        # try converting it to text
+        text = r.recognize_google(audio_listened, language='ja-JP')
+    return text
+
+# a function that splits the audio file into chunks on silence
+# and applies speech recognition
+def get_large_audio_transcription_on_silence(path):
+    """Splitting the large audio file into chunks
+    and apply speech recognition on each of these chunks"""
+    # open the audio file using pydub
+    sound = AudioSegment.from_file(path)  
+    # split audio sound where silence is 500 miliseconds or more and get chunks
+    chunks = split_on_silence(sound,
+        # experiment with this value for your target audio file
+        min_silence_len = 500,
+        # adjust this per requirement
+        silence_thresh = sound.dBFS-14,
+        # keep the silence for 1 second, adjustable as well
+        keep_silence=500,
+    )
+    folder_name = "audio-chunks"
+    # create a directory to store the audio chunks
+    if not os.path.isdir(folder_name):
+        os.mkdir(folder_name)
+    whole_text = ""
+    # process each chunk 
+    for i, audio_chunk in enumerate(chunks, start=1):
+        # export audio chunk and save it in
+        # the `folder_name` directory.
+        chunk_filename = os.path.join(folder_name, f"chunk{i}.wav")
+        audio_chunk.export(chunk_filename, format="wav")
+        # recognize the chunk
+        try:
+            text = transcribe_audio(chunk_filename)
+        except sr.UnknownValueError as e:
+            print("Error:", str(e))
+        else:
+            text = f"{text.capitalize()}. "
+            print(chunk_filename, ":", text)
+            whole_text += text
+    # return the text for all chunks detected
+    return whole_text