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Update app.py
Browse files
app.py
CHANGED
@@ -14,15 +14,27 @@ def play_text(text):
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os.system(f"start {temp_file.name}") # Windows
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return "β
Text is being read out. Please listen and read it yourself."
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# Function to transcribe user's audio
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def transcribe_audio(audio, original_text):
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recognizer = sr.Recognizer()
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with sr.AudioFile(audio) as source:
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audio_data = recognizer.record(source)
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try:
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start_time = time.time()
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#
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end_time = time.time()
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# Calculate Accuracy
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@@ -33,14 +45,19 @@ def transcribe_audio(audio, original_text):
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# Calculate speed
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duration = end_time - start_time # time to process (not speaking time)
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# Better: estimate speaking time from audio length if needed (advanced)
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speed = round(len(transcribed_words) / duration, 2) # words per second
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result = {
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"π Transcribed Text": transcription,
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"π― Accuracy (%)": accuracy,
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"β±οΈ Speaking Speed (words/sec)": speed
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}
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return result
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except Exception as e:
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@@ -67,5 +84,3 @@ with gr.Blocks() as app:
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# Launch the app
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app.launch()
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os.system(f"start {temp_file.name}") # Windows
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return "β
Text is being read out. Please listen and read it yourself."
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# Function to transcribe user's audio and compare with the original text
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def transcribe_audio(audio, original_text):
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recognizer = sr.Recognizer()
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with sr.AudioFile(audio) as source:
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audio_data = recognizer.record(source)
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try:
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start_time = time.time()
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# Split the audio into chunks (1-minute chunks in this example)
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audio_length = len(audio_data.frame_data)
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chunk_size = 60000 # 1 minute (60,000 ms)
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# Splitting audio data into chunks
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chunks = [audio_data.frame_data[i:i+chunk_size] for i in range(0, audio_length, chunk_size)]
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transcription = ""
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for chunk in chunks:
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audio_chunk = sr.AudioData(chunk, audio_data.sample_rate, audio_data.sample_width)
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# Using Google Speech Recognition (supports Hindi)
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transcription += recognizer.recognize_google(audio_chunk, language="hi-IN") + " "
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end_time = time.time()
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# Calculate Accuracy
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# Calculate speed
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duration = end_time - start_time # time to process (not speaking time)
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speed = round(len(transcribed_words) / duration, 2) # words per second
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# Compare words and highlight mistakes
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wrong_words = []
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for i, word in enumerate(original_words):
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if i >= len(transcribed_words) or word != transcribed_words[i]:
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wrong_words.append(f"π΄ {word}")
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result = {
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"π Transcribed Text": transcription,
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"π― Accuracy (%)": accuracy,
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"β±οΈ Speaking Speed (words/sec)": speed,
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"β Incorrect Words": ' '.join(wrong_words) if wrong_words else "None"
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}
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return result
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except Exception as e:
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# Launch the app
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app.launch()
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