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https://github.com/abdeladim-s/pywhispercpp

Python bindings for whisper.cpp
https://github.com/abdeladim-s/pywhispercpp

openai-whisper whisper-cpp

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Python bindings for whisper.cpp

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# pywhispercpp

Python bindings for [whisper.cpp](https://github.com/ggerganov/whisper.cpp) with a simple Pythonic API on top of it.



[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

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[![PyPi version](https://badgen.net/pypi/v/pywhispercpp)](https://pypi.org/project/pywhispercpp/)

[![Downloads](https://static.pepy.tech/badge/pywhispercpp)](https://pepy.tech/project/pywhispercpp)



whisper.cpp is:                       




High-performance inference of [OpenAI's Whisper](https://github.com/openai/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](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)



Supported platforms:



- [x] Mac OS (Intel and Arm)

- [x] [iOS](examples/whisper.objc)

- [x] [Android](examples/whisper.android)

- [x] Linux / [FreeBSD](https://github.com/ggerganov/whisper.cpp/issues/56#issuecomment-1350920264)

- [x] [WebAssembly](examples/whisper.wasm)

- [x] Windows ([MSVC](https://github.com/ggerganov/whisper.cpp/blob/master/.github/workflows/build.yml#L117-L144) and [MinGW](https://github.com/ggerganov/whisper.cpp/issues/168)]




# Table of contents



* [Installation](#installation)

    * [PYPI](#pypi-)

    * [From source](#from-source)

    * [CoreML support](#coreml-support)

* [Quick start](#quick-start)

* [Examples](#examples)

  * [Main](#main)

  * [Assistant](#assistant)

  * [Recording](#recording-)

  * [Live Stream Transcription](#live-stream-transcription)

* [Advanced usage](#advanced-usage)

* [Discussions and contributions](#discussions-and-contributions)

* [License](#license)



# Installation



First Install [ffmpeg](https://ffmpeg.org/)



 ```bash

 # 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

```



### PYPI 



2. Once ffmpeg is installed, install `pywhispercpp`



```shell

pip install pywhispercpp

```



If you want to use the examples, you will need to install extra dependencies



```shell

pip install pywhispercpp[examples]

```



### From source

You can install the latest dev version from GitHub:



```shell

git clone --recursive https://github.com/abdeladim-s/pywhispercpp

cd pywhispercpp

pip install .

```



### NVIDIA GPU support

1. Make sure you have `cuda` installed.

2. Install the package with CUDA support:



```shell

git clone --recursive https://github.com/abdeladim-s/pywhispercpp

cd pywhispercpp

WHISPER_CUDA=1 pip install .

```

### CoreML support



Thanks to [@tangm](https://github.com/tangm), using CoreML is now supported:



Clone the repository and install the package with `WHISPER_COREML=1`:



```shell

git clone --recursive https://github.com/abdeladim-s/pywhispercpp

cd pywhispercpp

WHISPER_COREML=1 pip install .

```



### Vulkan support



Thanks to [@thewh1teagle](https://github.com/thewh1teagle)



Clone the repository and install the package with `GGML_VULKAN=1`:



```shell

git clone --recursive https://github.com/abdeladim-s/pywhispercpp

cd pywhispercpp

GGML_VULKAN=1 pip install .

```



# Quick start



```python

from pywhispercpp.model import Model



model = Model('base.en', n_threads=6)

segments = model.transcribe('file.mp3')

for segment in segments:

    print(segment.text)

```



You can also assign a custom `new_segment_callback`



```python

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 `ggml` model will be downloaded automatically.

* You can pass any `whisper.cpp` [parameter](https://abdeladim-s.github.io/pywhispercpp/#pywhispercpp.constants.PARAMS_SCHEMA) as a keyword argument to the `Model` class or to the `transcribe` function.

* The `transcribe` function accepts any media file (audio/video), in any format.

* Check the [Model](https://abdeladim-s.github.io/pywhispercpp/#pywhispercpp.model.Model) class documentation for more details.



# Examples



The [examples folder](https://github.com/abdeladim-s/pywhispercpp/tree/main/pywhispercpp/examples) contains several examples inspired from the original [whisper.cpp/examples](https://github.com/ggerganov/whisper.cpp/tree/master/examples).



## Main

Just a straightforward example with a simple Command Line Interface. 



Check the source code [here](https://github.com/abdeladim-s/pywhispercpp/blob/main/pywhispercpp/examples/main.py), or use the CLI as follows:



```shell

pwcpp file.wav -m base --output-srt --print_realtime true

```

Run ```pwcpp --help``` to get the help message



```shell

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] [--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. , , , 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)

  --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



```



## Assistant



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](https://github.com/ggerganov/whisper.cpp/tree/master/examples/command) example.



You can check the source code [here](https://github.com/abdeladim-s/pywhispercpp/blob/main/pywhispercpp/examples/assistant.py) 

or you can use the class directly to create your own assistant:



```python

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



```shell

$ 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 30

```



## Recording 

Another simple [example](https://github.com/abdeladim-s/pywhispercpp/blob/main/pywhispercpp/examples/recording.py) to transcribe your own recordings.



You can use it from Python as follows:



```python

from pywhispercpp.examples.recording import Recording



myrec = Recording(5)

myrec.start()

```



Or from the command line:

    

```shell

$ 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.en

```

## Live Stream Transcription

This [example](https://github.com/abdeladim-s/pywhispercpp/blob/main/pywhispercpp/examples/livestream.py) 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!) :sweat_smile:



If you have a powerful machine, give it a try.



From python :

```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:



```shell

$ 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

```



# Advanced usage

* First check the [API documentation](https://abdeladim-s.github.io/pywhispercpp/) for more advanced usage.

* If you are a more experienced user, you can access the [C-Style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h) directly, almost all functions from `whisper.h`

are exposed with the binding module `_pywhispercpp`.



```python

import _pywhispercpp as pwcpp



ctx = pwcpp.whisper_init_from_file('path/to/ggml/model')

```



# Discussions and contributions

If you find any bug, please open an [issue](https://github.com/abdeladim-s/pywhispercpp/issues).



If you have any feedback, or you want to share how you are using this project, feel free to use the [Discussions](https://github.com/abdeladim-s/pywhispercpp/discussions) and open a new topic.



# License



This project is licensed under the same license as [whisper.cpp](https://github.com/ggerganov/whisper.cpp/blob/master/LICENSE) (MIT  [License](./LICENSE)).