Python bindings for whisper.cpp with a simple Pythonic API on top of it.
whisper.cpp is:
High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model:
- Plain C/C++ implementation without dependencies
- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
- AVX intrinsics support for x86 architectures
- VSX intrinsics support for POWER architectures
- Mixed F16 / F32 precision
- Low memory usage (Flash Attention)
- Zero memory allocations at runtime
- Runs on the CPU
- C-style API
Supported platforms:
First Install ffmpeg
# on Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg
# on Arch Linux
sudo pacman -S ffmpeg
# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg
# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg
# on Windows using Scoop (https://scoop.sh/)
scoop install ffmpeg- Once ffmpeg is installed, install
pywhispercpp
pip install pywhispercppIf you want to use the examples, you will need to install extra dependencies
pip install pywhispercpp[examples]You can install the latest dev version from GitHub:
pip install git+https://github.com/abdeladim-s/pywhispercppThanks to @tangm, using CoreML is now supported:
To build and install, clone the repository and run the following commands:
export CMAKE_ARGS="-DWHISPER_COREML=1"
python -m build --wheel # in this repository to build the wheel. Assumes you have installed build with pip install build
pip install dist/<generated>.whlThen download and convert the appropriate model using the original whisper.cpp repository, producing a <model>.mlmodelc directory.
You can now verify if everything's working:
from pywhispercpp.model import Model
model = Model('<model_path>/ggml-base.en.bin', n_threads=6)
print(Model.system_info()) # and you should see COREML = 1If successful, you should also see the following on your terminal:
whisper_init_state: loading Core ML model from '<model_path>/ggml-base.en-encoder.mlmodelc'
whisper_init_state: first run on a device may take a while ...
whisper_init_state: Core ML model loadedfrom pywhispercpp.model import Model
model = Model('base.en', n_threads=6)
segments = model.transcribe('file.mp3', speed_up=True)
for segment in segments:
print(segment.text)You can also assign a custom new_segment_callback
from pywhispercpp.model import Model
model = Model('base.en', print_realtime=False, print_progress=False)
segments = model.transcribe('file.mp3', new_segment_callback=print)- The
ggmlmodel will be downloaded automatically. - You can pass any
whisper.cppparameter as a keyword argument to theModelclass or to thetranscribefunction. - The
transcribefunction accepts any media file (audio/video), in any format. - Check the Model class documentation for more details.
The examples folder contains several examples inspired from the original whisper.cpp/examples.
Just a straightforward example with a simple Command Line Interface.
Check the source code here, or use the CLI as follows:
pwcpp file.wav -m base --output-srt --print_realtime trueRun pwcpp --help to get the help message
usage: pwcpp [-h] [-m MODEL] [--version] [--processors PROCESSORS] [-otxt] [-ovtt] [-osrt] [-ocsv] [--strategy STRATEGY]
[--n_threads N_THREADS] [--n_max_text_ctx N_MAX_TEXT_CTX] [--offset_ms OFFSET_MS] [--duration_ms DURATION_MS]
[--translate TRANSLATE] [--no_context NO_CONTEXT] [--single_segment SINGLE_SEGMENT] [--print_special PRINT_SPECIAL]
[--print_progress PRINT_PROGRESS] [--print_realtime PRINT_REALTIME] [--print_timestamps PRINT_TIMESTAMPS]
[--token_timestamps TOKEN_TIMESTAMPS] [--thold_pt THOLD_PT] [--thold_ptsum THOLD_PTSUM] [--max_len MAX_LEN]
[--split_on_word SPLIT_ON_WORD] [--max_tokens MAX_TOKENS] [--speed_up SPEED_UP] [--audio_ctx AUDIO_CTX]
[--prompt_tokens PROMPT_TOKENS] [--prompt_n_tokens PROMPT_N_TOKENS] [--language LANGUAGE] [--suppress_blank SUPPRESS_BLANK]
[--suppress_non_speech_tokens SUPPRESS_NON_SPEECH_TOKENS] [--temperature TEMPERATURE] [--max_initial_ts MAX_INITIAL_TS]
[--length_penalty LENGTH_PENALTY] [--temperature_inc TEMPERATURE_INC] [--entropy_thold ENTROPY_THOLD]
[--logprob_thold LOGPROB_THOLD] [--no_speech_thold NO_SPEECH_THOLD] [--greedy GREEDY] [--beam_search BEAM_SEARCH]
media_file [media_file ...]
positional arguments:
media_file The path of the media file or a list of filesseparated by space
options:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Path to the `ggml` model, or just the model name
--version show program's version number and exit
--processors PROCESSORS
number of processors to use during computation
-otxt, --output-txt output result in a text file
-ovtt, --output-vtt output result in a vtt file
-osrt, --output-srt output result in a srt file
-ocsv, --output-csv output result in a CSV file
--strategy STRATEGY Available sampling strategiesGreefyDecoder -> 0BeamSearchDecoder -> 1
--n_threads N_THREADS
Number of threads to allocate for the inferencedefault to min(4, available hardware_concurrency)
--n_max_text_ctx N_MAX_TEXT_CTX
max tokens to use from past text as prompt for the decoder
--offset_ms OFFSET_MS
start offset in ms
--duration_ms DURATION_MS
audio duration to process in ms
--translate TRANSLATE
whether to translate the audio to English
--no_context NO_CONTEXT
do not use past transcription (if any) as initial prompt for the decoder
--single_segment SINGLE_SEGMENT
force single segment output (useful for streaming)
--print_special PRINT_SPECIAL
print special tokens (e.g. <SOT>, <EOT>, <BEG>, etc.)
--print_progress PRINT_PROGRESS
print progress information
--print_realtime PRINT_REALTIME
print results from within whisper.cpp (avoid it, use callback instead)
--print_timestamps PRINT_TIMESTAMPS
print timestamps for each text segment when printing realtime
--token_timestamps TOKEN_TIMESTAMPS
enable token-level timestamps
--thold_pt THOLD_PT timestamp token probability threshold (~0.01)
--thold_ptsum THOLD_PTSUM
timestamp token sum probability threshold (~0.01)
--max_len MAX_LEN max segment length in characters
--split_on_word SPLIT_ON_WORD
split on word rather than on token (when used with max_len)
--max_tokens MAX_TOKENS
max tokens per segment (0 = no limit)
--speed_up SPEED_UP speed-up the audio by 2x using Phase Vocoder
--audio_ctx AUDIO_CTX
overwrite the audio context size (0 = use default)
--prompt_tokens PROMPT_TOKENS
tokens to provide to the whisper decoder as initial prompt
--prompt_n_tokens PROMPT_N_TOKENS
tokens to provide to the whisper decoder as initial prompt
--language LANGUAGE for auto-detection, set to None, "" or "auto"
--suppress_blank SUPPRESS_BLANK
common decoding parameters
--suppress_non_speech_tokens SUPPRESS_NON_SPEECH_TOKENS
common decoding parameters
--temperature TEMPERATURE
initial decoding temperature
--max_initial_ts MAX_INITIAL_TS
max_initial_ts
--length_penalty LENGTH_PENALTY
length_penalty
--temperature_inc TEMPERATURE_INC
temperature_inc
--entropy_thold ENTROPY_THOLD
similar to OpenAI's "compression_ratio_threshold"
--logprob_thold LOGPROB_THOLD
logprob_thold
--no_speech_thold NO_SPEECH_THOLD
no_speech_thold
--greedy GREEDY greedy
--beam_search BEAM_SEARCH
beam_search
This is a simple example showcasing the use of pywhispercpp as an assistant.
The idea is to use a VAD to detect speech (in this example we used webrtcvad), and when some speech is detected,
we run the transcription.
It is inspired from the whisper.cpp/examples/command example.
You can check the source code here or you can use the class directly to create your own assistant:
from pywhispercpp.examples.assistant import Assistant
my_assistant = Assistant(commands_callback=print, n_threads=8)
my_assistant.start()Here we set the commands_callback to a simple print, so the commands will just get printed on the screen.
You can run this example from the command line as well
$ pwcpp-assistant --help
usage: pwcpp-assistant [-h] [-m MODEL] [-ind INPUT_DEVICE] [-st SILENCE_THRESHOLD] [-bd BLOCK_DURATION]
options:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Whisper.cpp model, default to tiny.en
-ind INPUT_DEVICE, --input_device INPUT_DEVICE
Id of The input device (aka microphone)
-st SILENCE_THRESHOLD, --silence_threshold SILENCE_THRESHOLD
he duration of silence after which the inference will be running, default to 16
-bd BLOCK_DURATION, --block_duration BLOCK_DURATION
minimum time audio updates in ms, default to 30Another simple example to transcribe your own recordings.
You can use it from Python as follows:
from pywhispercpp.examples.recording import Recording
myrec = Recording(5)
myrec.start()Or from the command line:
$ pwcpp-recording --help
usage: pwcpp-recording [-h] [-m MODEL] duration
positional arguments:
duration duration in seconds
options:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Whisper.cpp model, default to tiny.enThis example is an attempt to transcribe a livestream in realtime, but the results are not quite satisfactory yet, the CPU jumps quickly to 100% and I cannot use huge models on my descent machine. (Or maybe I am doing something wrong!) 😅
If you have a powerful machine, give it a try.
From python :
from pywhispercpp.examples.livestream import LiveStream
url = "" # Make sure it is a direct stream URL
ls = LiveStream(url=url, n_threads=4)
ls.start()From the command line:
$ pwcpp-livestream --help
usage: pwcpp-livestream [-h] [-nt N_THREADS] [-m MODEL] [-od OUTPUT_DEVICE] [-bls BLOCK_SIZE] [-bus BUFFER_SIZE] [-ss SAMPLE_SIZE] url
positional arguments:
url Stream URL
options:
-h, --help show this help message and exit
-nt N_THREADS, --n_threads N_THREADS
number of threads, default to 3
-m MODEL, --model MODEL
Whisper.cpp model, default to tiny.en
-od OUTPUT_DEVICE, --output_device OUTPUT_DEVICE
the output device, aka the speaker, leave it None to take the default
-bls BLOCK_SIZE, --block_size BLOCK_SIZE
block size, default to 1024
-bus BUFFER_SIZE, --buffer_size BUFFER_SIZE
number of blocks used for buffering, default to 20
-ss SAMPLE_SIZE, --sample_size SAMPLE_SIZE
Sample size, default to 4- First check the API documentation for more advanced usage.
- If you are a more experienced user, you can access the C-Style API directly, almost all functions from
whisper.hare exposed with the binding module_pywhispercpp.
import _pywhispercpp as pwcpp
ctx = pwcpp.whisper_init_from_file('path/to/ggml/model')If you find any bug, please open an issue.
If you have any feedback, or you want to share how you are using this project, feel free to use the Discussions and open a new topic.
This project is licensed under the same license as whisper.cpp (MIT License).
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