Initial commit

This commit is contained in:
Yuta Hayashibe 2022-09-23 19:20:11 +09:00
commit 248d379078
21 changed files with 2232 additions and 0 deletions

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.github/FUNDING.yml vendored Normal file
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---
github: shirayu

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.github/dependabot.yml vendored Normal file
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---
version: 2
updates:
- package-ecosystem: "npm"
directory: "/"
schedule:
interval: "weekly"
- package-ecosystem: "pip"
directory: "/"
schedule:
interval: "weekly"
- package-ecosystem: "github-actions"
directory: "/"
schedule:
interval: "weekly"

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.github/workflows/ci.yml vendored Normal file
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---
name: CI
"on": push
jobs:
ci:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install Poetry Action
uses: snok/install-poetry@v1.3.1
with:
virtualenvs-create: true
virtualenvs-in-project: true
- name: Load cached venv
id: cached-poetry-dependencies
uses: actions/cache@v3
with:
path: .venv
key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}-${{ hashFiles('**/package-lock.json') }}
- run: pip install poetry
- run: poetry install
- uses: actions/setup-node@v3
with:
node-version: '14'
cache: npm
- run: npm install
- run: poetry run make -j $(nproc)

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.github/workflows/codeql-analysis.yml vendored Normal file
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---
name: "CodeQL"
"on":
push:
branches: [master]
pull_request:
branches: [master]
schedule:
- cron: '34 22 * * 2'
jobs:
analyze:
name: Analyze
runs-on: ubuntu-latest
permissions:
actions: read
contents: read
security-events: write
strategy:
fail-fast: false
matrix:
language: ['python']
steps:
- name: Checkout repository
uses: actions/checkout@v3
- name: Initialize CodeQL
uses: github/codeql-action/init@v2
with:
languages: ${{ matrix.language }}
- name: Autobuild
uses: github/codeql-action/autobuild@v2
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@v2

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---
# yamllint disable rule:line-length
name: Typos
on: # yamllint disable-line rule:truthy
push:
pull_request:
branches:
- '**'
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: typos-action
uses: crate-ci/typos@v1.12.7

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*.png
wheels
node_modules
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/

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.markdownlint.json Normal file
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{
"MD007": {
"indent": 4
},
"line-length": false,
"no-inline-html": false,
"MD026": false
}

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diff --git a/whisper_streaming/__init__.py b/whisper_streaming/__init__.py
index eb8bcb0..d246d29 100644
--- a/whisper_streaming/__init__.py
+++ b/whisper_streaming/__init__.py
@@ -25,6 +25,7 @@ class WhisperConfig(BaseModel):
logprob_threshold: Optional[float] = -1.0
no_captions_threshold: Optional[float] = 0.6
best_of: int = 5
+ beam_size: Optional[int] = None
class WhisperStreamingTranscriber:
@@ -42,7 +43,7 @@ class WhisperStreamingTranscriber:
self,
*,
t,
- beam_size,
+ beam_size: Optional[int],
patience,
best_of,
) -> DecodingOptions:
@@ -81,7 +82,7 @@ class WhisperStreamingTranscriber:
_decode_options: DecodingOptions = self._get_decoding_options(
t=t,
- beam_size=None,
+ beam_size=self.config.beam_size,
patience=0.0,
best_of=None,
)
@@ -100,7 +101,7 @@ class WhisperStreamingTranscriber:
t=t,
beam_size=None,
patience=None,
- best_of=None,
+ best_of=self.config.best_of,
)
retries: List[DecodingResult] = self.model.decode(
segment[needs_fallback], _decode_options # type: ignore
@@ -158,16 +159,25 @@ def get_opts() -> argparse.Namespace:
default="cuda" if torch.cuda.is_available() else "cpu",
help="device to use for PyTorch inference",
)
+ parser.add_argument(
+ "--beam_size",
+ "-b",
+ type=int,
+ default=5,
+ )
return parser.parse_args()
def main() -> None:
opts = get_opts()
+ if opts.beam_size <= 0:
+ opts.beam_size = None
config = WhisperConfig(
model_name=opts.model,
language=opts.language,
device=opts.device,
+ beam_size=opts.beam_size,
)
transcribe_from_mic(config=config)

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LICENSE Normal file
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MIT License
Copyright (c) 2022 shirayu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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LICENSE.whisper Normal file
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MIT License
Copyright (c) 2022 OpenAI
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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Makefile Normal file
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all: lint_node lint_python
TARGET_DIRS:=./whisper_streaming
flake8:
find $(TARGET_DIRS) | grep '\.py$$' | xargs flake8
black:
find $(TARGET_DIRS) | grep '\.py$$' | xargs black --diff | diff /dev/null -
isort:
find $(TARGET_DIRS) | grep '\.py$$' | xargs isort --diff | diff /dev/null -
pydocstyle:
find $(TARGET_DIRS) | grep -v tests | xargs pydocstyle --ignore=D100,D101,D102,D103,D104,D105,D107,D203,D212
yamllint:
find . \( -name node_modules -o -name .venv \) -prune -o -type f -name '*.yml' -print \
| xargs yamllint --no-warnings
lint_python: flake8 black isort pydocstyle
pyright:
npx pyright
markdownlint:
find . -type d \( -name node_modules -o -name .venv \) -prune -o -type f -name '*.md' -print \
| xargs npx markdownlint --config ./.markdownlint.json
lint_node: markdownlint pyright

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# whisper_streaming
[![CI](https://github.com/shirayu/whisper_streaming/actions/workflows/ci.yml/badge.svg)](https://github.com/shirayu/whisper_streaming/actions/workflows/ci.yml)
[![CodeQL](https://github.com/shirayu/whisper_streaming/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/shirayu/whisper_streaming/actions/workflows/codeql-analysis.yml)
[![Typos](https://github.com/shirayu/whisper_streaming/actions/workflows/typos.yml/badge.svg)](https://github.com/shirayu/whisper_streaming/actions/workflows/typos.yml)
Streaming transcriber with [whisper](https://github.com/openai/whisper)
## Example
```bash
git clone https://github.com/shirayu/whisper_streaming.git
cd whisper_streaming
poetry install --only main
poetry run whisper_streaming --language ja --model base
```
## Tips
If you get ``OSError: PortAudio library not found``: Install ``portaudio``
```bash
# Ubuntu
sudo apt-get install portaudio19-dev
```
## License
- [MIT License](LICENSE)
- Some codes are ported from the original whisper. Its license is also [MIT License](LICENSE.whisper)

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whisper = []

28
pyproject.toml Normal file
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[tool.poetry]
name = "whisper-streaming"
version = "0.1.0"
description = ""
authors = ["Yuta Hayashibe <yuta@hayashibe.jp>"]
readme = "README.md"
packages = [{include = "whisper_streaming"}]
[tool.poetry.dependencies]
python = ">=3.8,<4.0"
whisper = {git = "https://github.com/openai/whisper.git", rev = '15ab54826343c27cfaf44ce31e9c8fb63d0aa775'}
sounddevice = "^0.4.5"
pydantic = "^1.10.2"
[tool.poetry.group.dev.dependencies]
black = "^22.8.0"
isort = "^5.10.1"
flake8 = "^5.0.4"
pydocstyle = "^6.1.1"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.poetry.scripts]
whisper_streaming = "whisper_streaming.cli:main"

3
setup.cfg Normal file
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[flake8]
ignore = E203,W503,W504
max-line-length = 120

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91
whisper_streaming/cli.py Normal file
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#!/usr/bin/env python3
import argparse
import queue
from logging import INFO, getLogger
import sounddevice as sd
import torch
from whisper import available_models
from whisper.audio import N_FRAMES, SAMPLE_RATE
from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE
from whisper_streaming.schema import WhisperConfig
from whisper_streaming.transcriber import WhisperStreamingTranscriber
logger = getLogger(__name__)
def transcribe_from_mic(config: WhisperConfig) -> None:
sd_device = None
wsp = WhisperStreamingTranscriber(config=config)
q = queue.Queue()
def sd_callback(indata, frames, time, status):
if status:
logger.warning(status)
q.put(indata.ravel())
logger.info("Ready to transcribe")
with sd.InputStream(
samplerate=SAMPLE_RATE,
blocksize=N_FRAMES * 10, # FIXME
device=sd_device,
dtype="float32",
channels=1,
callback=sd_callback,
):
while True:
segment = q.get()
r = wsp.transcribe(segment=segment)
if r is not None:
print(r.text)
def get_opts() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"--language",
type=str,
default=None,
choices=sorted(LANGUAGES.keys())
+ sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]),
required=True,
)
parser.add_argument(
"--model",
type=str,
choices=available_models(),
required=True,
)
parser.add_argument(
"--device",
default="cuda" if torch.cuda.is_available() else "cpu",
help="device to use for PyTorch inference",
)
parser.add_argument(
"--beam_size",
"-b",
type=int,
default=5,
)
return parser.parse_args()
def main() -> None:
opts = get_opts()
logger.setLevel(INFO)
if opts.beam_size <= 0:
opts.beam_size = None
config = WhisperConfig(
model_name=opts.model,
language=opts.language,
device=opts.device,
beam_size=opts.beam_size,
)
transcribe_from_mic(config=config)
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
from typing import Optional, Tuple
from pydantic import BaseModel
class WhisperConfig(BaseModel):
model_name: str
device: str
language: str
temperatures: Tuple[float, ...] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0)
compression_ratio_threshold: Optional[float] = 2.4
logprob_threshold: Optional[float] = -1.0
no_captions_threshold: Optional[float] = 0.6
best_of: int = 5
beam_size: Optional[int] = None
no_speech_threshold: Optional[float] = 0.6
logprob_threshold: Optional[float] = -1.0
compression_ratio_threshold: Optional[float] = 2.4

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@ -0,0 +1,111 @@
#!/usr/bin/env python3
from typing import List, Optional
import numpy as np
import torch
from whisper import Whisper, load_model
from whisper.audio import N_FRAMES, log_mel_spectrogram, pad_or_trim
from whisper.decoding import DecodingOptions, DecodingResult
from whisper.tokenizer import get_tokenizer
from whisper_streaming.schema import WhisperConfig
class WhisperStreamingTranscriber:
def __init__(self, *, config: WhisperConfig):
self.config: WhisperConfig = config
self.model: Whisper = load_model(config.model_name, device=config.device)
self.tokenizer = get_tokenizer(
self.model.is_multilingual,
language=config.language,
task="transcribe",
)
self.dtype = torch.float16
def _get_decoding_options(
self,
*,
t,
beam_size: Optional[int],
patience: float,
best_of: Optional[int],
) -> DecodingOptions:
return DecodingOptions(
task="transcribe",
language=None,
temperature=t,
sample_len=None,
best_of=best_of,
beam_size=beam_size,
patience=patience,
length_penalty=None,
prompt=None,
prefix=None,
suppress_blank=True,
suppress_tokens="-1",
without_timestamps=False,
max_initial_timestamp=0.0,
fp16=True,
)
def _decode_with_fallback(self, *, segment: np.ndarray) -> List[DecodingResult]:
assert len(self.config.temperatures) >= 1
t = self.config.temperatures[0]
_decode_options1: DecodingOptions = self._get_decoding_options(
t=t,
beam_size=self.config.beam_size,
patience=0.0,
best_of=None,
)
results: List[DecodingResult] = self.model.decode(segment, _decode_options1) # type: ignore
for t in self.config.temperatures[1:]:
needs_fallback = [
self.config.compression_ratio_threshold is not None
and result.compression_ratio > self.config.compression_ratio_threshold
or self.config.logprob_threshold is not None
and result.avg_logprob < self.config.logprob_threshold
for result in results
]
if any(needs_fallback):
_decode_options2: DecodingOptions = self._get_decoding_options(
t=t,
beam_size=None,
patience=0.0,
best_of=self.config.best_of,
)
retries: List[DecodingResult] = self.model.decode(
segment[needs_fallback], _decode_options2 # type: ignore
)
for retry_index, original_index in enumerate(
np.nonzero(needs_fallback)[0]
):
results[original_index] = retries[retry_index]
return results
def transcribe(
self,
*,
segment: np.ndarray,
) -> Optional[DecodingResult]:
log_spec = log_mel_spectrogram(audio=segment).unsqueeze(0)
segment = (
pad_or_trim(log_spec, N_FRAMES)
.to(self.model.device) # type:ignore
.to(self.dtype) # type:ignore
)
results = self._decode_with_fallback(segment=segment)
result = results[0]
if self.config.no_speech_threshold is not None:
if (result.no_speech_prob > self.config.no_speech_threshold) and not (
self.config.logprob_threshold is not None
and result.avg_logprob > self.config.logprob_threshold
):
return
# FIXME: work with timestamp
return result