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126 changes: 126 additions & 0 deletions
126
llama_assistant/speech_recognition_whisper_experimental.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| import threading | ||
| import queue | ||
| import pyaudio | ||
| import wave | ||
| import os | ||
| from pathlib import Path | ||
| import datetime | ||
| from whisper_cpp_python import Whisper | ||
| import re | ||
| import requests | ||
|
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| class SpeechRecognition: | ||
| def __init__(self): | ||
| # Audio settings | ||
| self.RATE = 16000 | ||
| self.CHUNK = self.RATE | ||
| self.NB_CHANNELS = 1 | ||
| self.RECORD_SECONDS = 1 | ||
|
|
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| # Whisper settings | ||
| self.WHISPER_LANGUAGE = "en" | ||
| self.WHISPER_THREADS = 1 | ||
|
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| # Initialize queues | ||
| self.audio_queue = queue.Queue() | ||
| self.text_queue = queue.Queue() | ||
|
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| # Set up model path and download if necessary | ||
| self.model_dir = Path.home() / "llama-assistant" / "models" / "whisper-cpp" | ||
| self.model_path = self.model_dir / "ggml-tiny-fp16.bin" | ||
| self.download_model_if_needed() | ||
|
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| # Initialize Whisper model | ||
| self.whisper = Whisper(model_path=str(self.model_path), n_threads=self.WHISPER_THREADS) | ||
|
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| # Initialize PyAudio | ||
| self.audio = pyaudio.PyAudio() | ||
| self.stream = self.audio.open( | ||
| format=pyaudio.paInt16, | ||
| channels=self.NB_CHANNELS, | ||
| rate=self.RATE, | ||
| input=True, | ||
| frames_per_buffer=self.CHUNK, | ||
| ) | ||
|
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| # Create temporary folder for audio files | ||
| self.tmp_audio_folder = Path("./tmp_audio") | ||
| if not self.tmp_audio_folder.exists(): | ||
| self.tmp_audio_folder.mkdir() | ||
|
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| self.stop_listening = False | ||
|
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| def download_model_if_needed(self): | ||
| if not self.model_path.exists(): | ||
| print("Downloading Whisper model...") | ||
| self.model_dir.mkdir(parents=True, exist_ok=True) | ||
| url = "https://huggingface.co/danielus/ggml-whisper-models/resolve/main/ggml-tiny-fp16.bin" | ||
| response = requests.get(url) | ||
| with open(self.model_path, "wb") as f: | ||
| f.write(response.content) | ||
| print("Model downloaded successfully.") | ||
|
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| def listen(self): | ||
| while not self.stop_listening: | ||
| audio_data = self.stream.read(self.CHUNK) | ||
| self.audio_queue.put(audio_data) | ||
|
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| def transcribe(self): | ||
| while not self.stop_listening: | ||
| if not self.audio_queue.empty(): | ||
| audio_data = self.audio_queue.get() | ||
|
|
||
| # Save audio data to temporary file | ||
| tmp_filepath = f"./tmp_audio/output_{datetime.datetime.now()}.wav" | ||
| with wave.open(tmp_filepath, "wb") as wf: | ||
| wf.setnchannels(self.NB_CHANNELS) | ||
| wf.setsampwidth(2) # 16-bit audio | ||
| wf.setframerate(self.RATE) | ||
| wf.writeframes(audio_data) | ||
|
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| # Transcribe audio | ||
| res = self.whisper.transcribe(file=tmp_filepath, language=self.WHISPER_LANGUAGE) | ||
| transcription = res["text"] | ||
|
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| # Clean up transcription | ||
| transcription = re.sub(r"\[.*\]", "", transcription) | ||
| transcription = re.sub(r"\(.*\)", "", transcription) | ||
|
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| # Add transcription to text queue | ||
| self.text_queue.put(transcription) | ||
|
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| # Cleanup | ||
| os.remove(tmp_filepath) | ||
|
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| def start(self): | ||
| self.stop_listening = False | ||
| threading.Thread(target=self.listen, daemon=True).start() | ||
| threading.Thread(target=self.transcribe, daemon=True).start() | ||
|
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| def stop(self): | ||
| self.stop_listening = True | ||
| self.stream.stop_stream() | ||
| self.stream.close() | ||
| self.audio.terminate() | ||
|
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| def get_transcription(self): | ||
| if not self.text_queue.empty(): | ||
| return self.text_queue.get() | ||
| return None | ||
|
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|
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| # Example usage | ||
| if __name__ == "__main__": | ||
| recognizer = SpeechRecognition() | ||
| recognizer.start() | ||
|
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| print("Speech recognition started. Press Ctrl+C to stop.") | ||
| try: | ||
| while True: | ||
| transcription = recognizer.get_transcription() | ||
| if transcription: | ||
| print(f"Transcription: {transcription}") | ||
| except KeyboardInterrupt: | ||
| print("Stopping speech recognition...") | ||
| recognizer.stop() |
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