Python wrapper for whisper.cpp
-
Lightweight, using
ctypes.CDLLto call functions from thelibwhispershared library. -
Migrate the
whisper-streamfunctions to deal with live streaming case for async-processing
Clone whisper.cpp, then build it
# clone whisper.cpp
git clone https://github.com/ggml-org/whisper.cpp
# nevagation into this folder
cd whisper.cpp/
# checkout the stable version (current supporting)
git checkout v1.8.0
# build whisper.cpp
cmake -B build
cmake --build build --config ReleaseDownload ggml models
# ASR model
sh ./models/download-ggml-model.sh [tiny|base|small|large]
# VAD model
sh ./models/download-vad-model.sh silero-v5.1.2Install from source:
pip install git+https://github.com/fann1993814/whisper.cpyFollow below steps, and trace trancribe.py
# WHISPER_CPP_PATH is the whisper.cpp project location
audio_wav = f"{WHISPER_CPP_PATH}/samples/jfk.wav"
asr_model_path = f"{WHISPER_CPP_PATH}/models/ggml-tiny.bin"
vad_model_path = f"{WHISPER_CPP_PATH}/models/ggml-silero-v5.1.2.bin"
library_path = f"{WHISPER_CPP_PATH}/build/src/libwhisper.dylib" # Mac: dylib, Linux: so, Win: dllimport soundfile as sf
data, sr = sf.read(audio_wav, dtype='float32')from whispercpy import WhisperCPP
from whispercpy.utils import to_timestamp
model = WhisperCPP(library_path, asr_model_path,
vad_model_path, use_gpu=True, verbose=False)
print('--------- Lib Version ---------')
print(model.get_version())
# --------- Lib Version ---------
# Ver: 1.8.0
print('--------- VAD Result ----------')
for segment in model.vad(data):
print(f'[{to_timestamp(segment.t0, False)}' +
" --> " + f'{to_timestamp(segment.t1, False)}]')
# --------- VAD Result ----------
# [00:00:00.290 --> 00:00:02.210]
# [00:00:03.300 --> 00:00:03.770]
# [00:00:04.000 --> 00:00:04.350]
# [00:00:05.380 --> 00:00:07.650]
# [00:00:08.160 --> 00:00:10.590]
print('--------- ASR Result ----------')
for segment in model.transcribe(data, language='en', beam_size=5, token_timestamps=True):
print(f'[{to_timestamp(segment.t0, False)}' +
" --> " + f'{to_timestamp(segment.t1, False)}] ' + segment.text)
print('--------- Token Info ----------')
print('\n'.join([f'[{to_timestamp(token.t0, False)}' +
" --> " + f'{to_timestamp(token.t1, False)}] {token.text}' for token in segment.tokens]))
print('-------------------------------')
# --------- ASR Result ----------
# [00:00:00.000 --> 00:00:10.400] And so, my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
# --------- Token Info ----------
# [00:00:00.000 --> 00:00:00.000] [_BEG_]
# [00:00:00.320 --> 00:00:00.320] And
# [00:00:00.330 --> 00:00:00.530] so
# [00:00:00.680 --> 00:00:00.740] ,
# [00:00:00.740 --> 00:00:00.950] my
# [00:00:00.950 --> 00:00:01.590] fellow
# [00:00:01.590 --> 00:00:02.100] Americans
# [00:00:02.550 --> 00:00:03.000] ,
# [00:00:03.290 --> 00:00:03.650] ask
# [00:00:04.010 --> 00:00:04.280] not
# [00:00:04.650 --> 00:00:05.200] what
# [00:00:05.410 --> 00:00:05.560] your
# [00:00:05.650 --> 00:00:06.410] country
# [00:00:06.410 --> 00:00:06.750] can
# [00:00:06.750 --> 00:00:06.920] do
# [00:00:07.010 --> 00:00:07.490] for
# [00:00:07.490 --> 00:00:07.970] you
# [00:00:08.170 --> 00:00:08.170] ,
# [00:00:08.190 --> 00:00:08.430] ask
# [00:00:08.430 --> 00:00:08.750] what
# [00:00:08.910 --> 00:00:09.040] you
# [00:00:09.040 --> 00:00:09.350] can
# [00:00:09.350 --> 00:00:09.500] do
# [00:00:09.500 --> 00:00:09.710] for
# [00:00:09.720 --> 00:00:09.980] your
# [00:00:09.990 --> 00:00:10.350] country
# [00:00:10.470 --> 00:00:10.500] .
# [00:00:10.500 --> 00:00:10.500] [_TT_525]
# -------------------------------to_timestampcan translate the time unit from whisper.cpp into a formal repesenation
Follow below steps, and trace live.py
Note: for realtime inference,
tiny/base/smallfor cpumedium/large/large-v2/large-v3for gpu
from whispercpy import WhisperCPP, WhisperStream
core = WhisperCPP(lib_path, model_path, use_gpu=False)
asr = WhisperStream(core, language='en', return_token=True)count = 0
def callback(indata, frames, time, status):
global count
chunk = indata.copy().tobytes()
asr.pipe(chunk)
transcript = asr.get_transcript()
transcripts = asr.get_transcripts()
if len(transcripts) > count:
print("\r"+transcripts[-1].text)
print('--')
count += 1
else:
print(f"\r{transcript.text}", end="", flush=True)asr.pipe: a threading function for async to process audio for transcribing continuouslyasr.get_transcript: get the current transcirptionasr.get_transcripts: get whole transcirptions
import sounddevice as sd
from whispercpy.constant import STREAMING_ENDING
samplerate = 16000
block_duration = 0.25
block_size = int(samplerate * block_duration)
channels = 1
# Recording
try:
with sd.InputStream(
samplerate=samplerate,
channels=channels,
callback=callback, blocksize=block_size, dtype='float32'):
print("🎤 Recording for ASR... Press Ctrl+C to stop.")
while True:
sd.sleep(1000)
except KeyboardInterrupt:
print("⏹️ Recording stopped.")
# send end signal
asr.pipe(STREAMING_ENDING).join()
# Result
#
# 🎤 Recording for ASR... Press Ctrl+C to stop.
# This is my voice test.
# --
# Can you hear me?
# --
# ^C⏹️ Recording stopped.
# [00:00:00.800 --> 00:00:11.000] This is my voice test.
# -------------------------------
# [00:00:00.800 --> 00:00:00.800] [_BEG_]
# [00:00:00.800 --> 00:00:01.490] This
# [00:00:01.850 --> 00:00:01.850] is
# [00:00:01.890 --> 00:00:02.200] my
# [00:00:02.200 --> 00:00:03.080] voice
# [00:00:03.080 --> 00:00:03.710] test
# [00:00:03.710 --> 00:00:08.290] .
# [00:00:08.300 --> 00:00:11.000] [_TT_150]
# -------------------------------
# [00:00:11.300 --> 00:00:21.500] Can you hear me?
# -------------------------------
# [00:00:11.300 --> 00:00:11.300] [_BEG_]
# [00:00:11.300 --> 00:00:11.790] Can
# [00:00:12.050 --> 00:00:12.280] you
# [00:00:12.280 --> 00:00:12.940] hear
# [00:00:12.940 --> 00:00:13.070] me
# [00:00:13.070 --> 00:00:17.960] ?
# [00:00:17.960 --> 00:00:21.500] [_TT_100]
# -------------------------------STREAMING_ENDING: a singal for stopping transcribing, and usejoin()for waiting last thread complete.
This project follows whisper.cpp license as MIT