Create app.py

app.py

@@ -0,0 +1,179 @@
+import argparse
+import io
+import os
+import speech_recognition as sr
+import whisper
+import torch
+
+from datetime import datetime, timedelta
+from queue import Queue
+from tempfile import NamedTemporaryFile
+from time import sleep
+from sys import platform
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        default="medium",
+        help="Model to use",
+        choices=["tiny", "base", "small", "medium", "large"],
+    )
+    parser.add_argument(
+        "--non_english", action="store_true", help="Don't use the english model."
+    )
+    parser.add_argument(
+        "--energy_threshold",
+        default=1000,
+        help="Energy level for mic to detect.",
+        type=int,
+    )
+    parser.add_argument(
+        "--record_timeout",
+        default=2,
+        help="How real time the recording is in seconds.",
+        type=float,
+    )
+    parser.add_argument(
+        "--phrase_timeout",
+        default=3,
+        help="How much empty space between recordings before we "
+        "consider it a new line in the transcription.",
+        type=float,
+    )
+    if "linux" in platform:
+        parser.add_argument(
+            "--default_microphone",
+            default="pulse",
+            help="Default microphone name for SpeechRecognition. "
+            "Run this with 'list' to view available Microphones.",
+            type=str,
+        )
+    args = parser.parse_args()
+
+    # The last time a recording was retreived from the queue.
+    phrase_time = None
+    # Current raw audio bytes.
+    last_sample = bytes()
+    # Thread safe Queue for passing data from the threaded recording callback.
+    data_queue = Queue()
+    # We use SpeechRecognizer to record our audio because it has a nice feauture where it can detect when speech ends.
+    recorder = sr.Recognizer()
+    recorder.energy_threshold = args.energy_threshold
+    # Definitely do this, dynamic energy compensation lowers the energy threshold dramtically to a point where the SpeechRecognizer never stops recording.
+    recorder.dynamic_energy_threshold = False
+
+    # Important for linux users.
+    # Prevents permanent application hang and crash by using the wrong Microphone
+    if "linux" in platform:
+        mic_name = args.default_microphone
+        if not mic_name or mic_name == "list":
+            print("Available microphone devices are: ")
+            for index, name in enumerate(sr.Microphone.list_microphone_names()):
+                print(f'Microphone with name "{name}" found')
+            return
+        else:
+            for index, name in enumerate(sr.Microphone.list_microphone_names()):
+                if mic_name in name:
+                    source = sr.Microphone(sample_rate=16000, device_index=index)
+                    break
+    else:
+        source = sr.Microphone(sample_rate=16000)
+
+    # Load / Download model
+    model = args.model
+    if args.model != "large" and not args.non_english:
+        model = model + ".en"
+    audio_model = whisper.load_model(model)
+
+    record_timeout = args.record_timeout
+    phrase_timeout = args.phrase_timeout
+
+    temp_file = NamedTemporaryFile().name
+    transcription = [""]
+
+    with source:
+        recorder.adjust_for_ambient_noise(source)
+
+    def record_callback(_, audio: sr.AudioData) -> None:
+        """
+        Threaded callback function to recieve audio data when recordings finish.
+        audio: An AudioData containing the recorded bytes.
+        """
+        # Grab the raw bytes and push it into the thread safe queue.
+        data = audio.get_raw_data()
+        data_queue.put(data)
+
+    # Create a background thread that will pass us raw audio bytes.
+    # We could do this manually but SpeechRecognizer provides a nice helper.
+    recorder.listen_in_background(
+        source, record_callback, phrase_time_limit=record_timeout
+    )
+
+    # Cue the user that we're ready to go.
+    print("Model loaded.\n")
+
+    while True:
+        try:
+            now = datetime.utcnow()
+            # Pull raw recorded audio from the queue.
+            if not data_queue.empty():
+                phrase_complete = False
+                # If enough time has passed between recordings, consider the phrase complete.
+                # Clear the current working audio buffer to start over with the new data.
+                if phrase_time and now - phrase_time > timedelta(
+                    seconds=phrase_timeout
+                ):
+                    last_sample = bytes()
+                    phrase_complete = True
+                # This is the last time we received new audio data from the queue.
+                phrase_time = now
+
+                # Concatenate our current audio data with the latest audio data.
+                while not data_queue.empty():
+                    data = data_queue.get()
+                    last_sample += data
+
+                # Use AudioData to convert the raw data to wav data.
+                audio_data = sr.AudioData(
+                    last_sample, source.SAMPLE_RATE, source.SAMPLE_WIDTH
+                )
+                wav_data = io.BytesIO(audio_data.get_wav_data())
+
+                # Write wav data to the temporary file as bytes.
+                with open(temp_file, "w+b") as f:
+                    f.write(wav_data.read())
+
+                # Read the transcription.
+                result = audio_model.transcribe(
+                    temp_file, fp16=torch.cuda.is_available()
+                )
+                text = result["text"].strip()
+
+                # If we detected a pause between recordings, add a new item to our transcripion.
+                # Otherwise edit the existing one.
+                if phrase_complete:
+                    transcription.append(text)
+                else:
+                    transcription[-1] = text
+
+                # Clear the console to reprint the updated transcription.
+                os.system("cls" if os.name == "nt" else "clear")
+                for line in transcription:
+                    print(line)
+                # Flush stdout.
+                print("", end="", flush=True)
+
+                # Infinite loops are bad for processors, must sleep.
+                sleep(0.25)
+        except KeyboardInterrupt:
+            break
+
+    print("\n\nTranscription:")
+    for line in transcription:
+        print(line)
+
+
+if __name__ == "__main__":
+    main()