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Use WhisperCPP as offline speech recognizer
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,138 @@ | ||
| import pkgutil | ||
| from pathlib import Path | ||
| import datetime | ||
| import os | ||
| import re | ||
| import requests | ||
|
|
||
| from PyQt6.QtCore import QThread, pyqtSignal | ||
| import speech_recognition as sr | ||
|
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| # patch whisper on file not find error | ||
| # https://github.com/carloscdias/whisper-cpp-python/pull/12 | ||
| try: | ||
| import whisper_cpp_python | ||
| except FileNotFoundError: | ||
| regex = r"(\"darwin\":\n\s*lib_ext = \")\.so(\")" | ||
| subst = "\\1.dylib\\2" | ||
|
|
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| print("fixing and re-importing whisper_cpp_python...") | ||
| # load whisper_cpp_python and substitute .so with .dylib for darwin | ||
| package = pkgutil.get_loader("whisper_cpp_python") | ||
| whisper_path = Path(package.path) | ||
| whisper_cpp_py = whisper_path.parent.joinpath("whisper_cpp.py") | ||
| content = whisper_cpp_py.read_text() | ||
| result = re.sub(regex, subst, content, 0, re.MULTILINE) | ||
| whisper_cpp_py.write_text(result) | ||
|
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| import whisper_cpp_python | ||
|
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|
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| class SpeechRecognitionThread(QThread): | ||
| finished = pyqtSignal(str) | ||
| error = pyqtSignal(str) | ||
| WHISPER_THREADS = 4 | ||
| WHISPER_LANGUAGE = "en" | ||
|
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| def __init__(self): | ||
| super().__init__() | ||
| self.stop_listening = False | ||
|
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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-base-fp16.bin" | ||
| self.download_model_if_needed() | ||
|
|
||
| # Initialize Whisper model | ||
| self.whisper = whisper_cpp_python.Whisper( | ||
| model_path=str(self.model_path), n_threads=self.WHISPER_THREADS | ||
| ) | ||
|
|
||
| # Create temporary folder for audio files | ||
| self.tmp_audio_folder = Path.home() / "llama-assistant" / "tmp_audio" | ||
| self.tmp_audio_folder.mkdir(parents=True, exist_ok=True) | ||
|
|
||
| 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-base-fp16.bin" | ||
| response = requests.get(url) | ||
| with open(self.model_path, "wb") as f: | ||
| f.write(response.content) | ||
| print("Model downloaded successfully.") | ||
|
|
||
| def run(self): | ||
| recognizer = sr.Recognizer() | ||
| microphone = sr.Microphone() | ||
| try: | ||
| with microphone as source: | ||
| recognizer.adjust_for_ambient_noise(source) | ||
| while not self.stop_listening: | ||
| try: | ||
| recognizer.pause_threshold = 1 | ||
| audio_data = recognizer.listen(source, timeout=1, phrase_time_limit=5) | ||
|
|
||
| # Save audio data to temporary file | ||
| tmp_filepath = ( | ||
| self.tmp_audio_folder / f"temp_audio_{datetime.datetime.now()}.wav" | ||
| ) | ||
| with open(tmp_filepath, "wb") as f: | ||
| f.write(audio_data.get_wav_data()) | ||
|
|
||
| # Transcribe audio | ||
| res = self.whisper.transcribe( | ||
| file=tmp_filepath, language=self.WHISPER_LANGUAGE | ||
| ) | ||
| transcription = res["text"] | ||
|
|
||
| # Clean up transcription | ||
| transcription = re.sub(r"\[.*\]", "", transcription) | ||
| transcription = re.sub(r"\(.*\)", "", transcription) | ||
|
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| print(f"Transcription: {transcription}") | ||
| os.remove(tmp_filepath) | ||
|
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| self.finished.emit(transcription) | ||
| except sr.WaitTimeoutError: | ||
| print("timeout") | ||
| continue | ||
| except sr.UnknownValueError: | ||
| print("Could not understand audio") | ||
| self.error.emit("Could not understand audio") | ||
| except sr.RequestError as e: | ||
| print(f"Could not request results; {e}") | ||
| self.error.emit(f"Could not request results; {e}") | ||
| except KeyboardInterrupt: | ||
| print("Keyboard interrupt detected. Stopping speech recognition.") | ||
| self.stop() | ||
|
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| def stop(self): | ||
| self.stop_listening = True | ||
|
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|
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| # Demo code | ||
| if __name__ == "__main__": | ||
| from PyQt6.QtWidgets import QApplication | ||
| import sys | ||
|
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| app = QApplication(sys.argv) | ||
|
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| def on_finished(text): | ||
| print(f"Transcription: {text}") | ||
| thread.stop() | ||
| app.quit() | ||
|
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| def on_error(error_message): | ||
| print(f"Error: {error_message}") | ||
| thread.stop() | ||
| app.quit() | ||
|
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| thread = SpeechRecognitionThread() | ||
| thread.finished.connect(on_finished) | ||
| thread.error.connect(on_error) | ||
|
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| print("Starting speech recognition. Speak into your microphone...") | ||
| thread.start() | ||
|
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||
| sys.exit(app.exec()) |
126 changes: 0 additions & 126 deletions
126
llama_assistant/speech_recognition_whisper_experimental.py
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