Formatting and spelling fixes.
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@ -33,16 +33,16 @@ def main():
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"Run this with 'list' to view available Microphones.", type=str)
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args = parser.parse_args()
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# The last time a recording was retreived from the queue.
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# The last time a recording was retrieved from the queue.
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phrase_time = None
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# Current raw audio bytes.
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last_sample = bytes()
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# Thread safe Queue for passing data from the threaded recording callback.
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data_queue = Queue()
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# We use SpeechRecognizer to record our audio because it has a nice feauture where it can detect when speech ends.
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# We use SpeechRecognizer to record our audio because it has a nice feature where it can detect when speech ends.
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recorder = sr.Recognizer()
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recorder.energy_threshold = args.energy_threshold
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# Definitely do this, dynamic energy compensation lowers the energy threshold dramtically to a point where the SpeechRecognizer never stops recording.
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# Definitely do this, dynamic energy compensation lowers the energy threshold dramatically to a point where the SpeechRecognizer never stops recording.
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recorder.dynamic_energy_threshold = False
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# Important for linux users.
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@ -79,7 +79,7 @@ def main():
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def record_callback(_, audio:sr.AudioData) -> None:
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"""
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Threaded callback function to recieve audio data when recordings finish.
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Threaded callback function to receive audio data when recordings finish.
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audio: An AudioData containing the recorded bytes.
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"""
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# Grab the raw bytes and push it into the thread safe queue.
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@ -124,7 +124,7 @@ def main():
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result = audio_model.transcribe(temp_file, fp16=torch.cuda.is_available())
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text = result['text'].strip()
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# If we detected a pause between recordings, add a new item to our transcripion.
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# If we detected a pause between recordings, add a new item to our transcription.
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# Otherwise edit the existing one.
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if phrase_complete:
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transcription.append(text)
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