diff options
Diffstat (limited to 'Scripts')
| -rw-r--r-- | Scripts/requirements.txt | 2 | ||||
| -rw-r--r-- | Scripts/transcribe.py | 53 |
2 files changed, 18 insertions, 37 deletions
diff --git a/Scripts/requirements.txt b/Scripts/requirements.txt index c218302..043fb40 100644 --- a/Scripts/requirements.txt +++ b/Scripts/requirements.txt @@ -9,4 +9,4 @@ pyyaml --extra-index-url https://download.pytorch.org/whl/cu116 torch==1.13.1+cu116 -git+https://github.com/openai/whisper.git +faster-whisper diff --git a/Scripts/transcribe.py b/Scripts/transcribe.py index 6793336..208bcd1 100644 --- a/Scripts/transcribe.py +++ b/Scripts/transcribe.py @@ -2,6 +2,7 @@ from datetime import datetime from emotes_v2 import EmotesState +from faster_whisper import WhisperModel from functools import partial from playsound import playsound @@ -18,7 +19,6 @@ import sys import threading import time import wave -import whisper class Config: def __init__(self): @@ -72,7 +72,7 @@ class AudioState: # The language the user is speaking in. Default is English but user may set # this to whatever they want. - self.language = whisper.tokenizer.TO_LANGUAGE_CODE["english"] + self.language = "en" self.audio_paused = False @@ -210,38 +210,19 @@ def transcribe(audio_state, model, frames, use_cpu: bool): frames = np.asarray(audio_state.frames) audio = np.frombuffer(frames, np.int16).flatten().astype(np.float32) / 32768.0 - audio = whisper.pad_or_trim(audio, length = audio_state.RATE * - audio_state.MAX_LENGTH_S_WHISPER) + segments, info = model.transcribe(audio, beam_size=5, + language=audio_state.language) - mel = whisper.log_mel_spectrogram(audio).to(model.device) - - result = None - #for temp in (0.00, 0.05, 0.10, 0.15, 0.20): - #for temp in (0.00, 0.05): - for temp in (0.00,): - use_gpu = not use_cpu - options = whisper.DecodingOptions(language = audio_state.language, - beam_size = 5, temperature = temp, without_timestamps = True, - fp16 = use_gpu) - result = whisper.decode(model, mel, options) - - if result.avg_logprob < -1.0: - print("avg logprob: {}".format(result.avg_logprob)) - result = None - continue - - if result.compression_ratio > 2.4: - print("compression ratio: {}".format(result.compression_ratio)) - result = None - continue - - if result.no_speech_prob > 0.60: - print("no speech prob: {}".format(result.no_speech_prob)) - result = None - continue + result = "" + for s in segments: + print(f" s: {s}") + print(f" s.text: {s.text}") + if (len(result) == 0): + result = str(s.text) + else: + result += " " + str(s.text) - result = result.text - break + print(f"Result: {result}") return result @@ -391,7 +372,7 @@ def transcribeLoop(mic: str, language: str, model: str, enable_local_beep: bool, use_cpu: bool, use_builtin: bool, button: str, estate: EmotesState): audio_state = getMicStream(mic) - audio_state.language = whisper.tokenizer.TO_LANGUAGE_CODE[language] + audio_state.language = language print("Safe to start talking") @@ -400,7 +381,7 @@ def transcribeLoop(mic: str, language: str, model: str, model_root = os.path.join(dname, "Models") print("Model {} will be saved to {}".format(model, model_root)) - model = whisper.load_model(model, download_root=model_root) + model = WhisperModel("large-v2", device="cuda", compute_type="float16") transcribe_audio_thd = threading.Thread(target = transcribeAudio, args = [audio_state, model, use_cpu]) transcribe_audio_thd.daemon = True @@ -442,7 +423,7 @@ if __name__ == "__main__": dname = os.path.dirname(abspath) dname = os.path.dirname(dname) dname = os.path.dirname(dname) - os.chdir(dname) + #os.chdir(dname) print(f"Set cwd to {os.getcwd()}") parser = argparse.ArgumentParser() @@ -468,7 +449,7 @@ if __name__ == "__main__": args.language = "english" if not args.model: - args.language = "base" + args.model = "base" if not args.bytes_per_char or not args.chars_per_sync: print("--bytes_per_char and --chars_per_sync required", file=sys.stderr) |