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compare: datasci:5d93fb7ad3254838b11fe181d015d5e6d8fb9abb
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datasci:main

10 commits
64123a29e0 ... 5d93fb7ad3

Author SHA1 Message Date
Marty Oehme
5d93fb7ad3
Default to english 2024-03-14 08:43:16 +01:00
Marty Oehme
56d7406a31
Add logging to diarization 2023-08-23 17:11:47 +02:00
Marty Oehme
a79f825f66
Fix speaker rendered throughout paragraph 
Each sentence or 'segment' in whisper would be preceded by a [speaker]
notation. This commit fixes that to only include the speaker in front
of a larger group (since a new speaker would start a new diarization
group this will always work).
 2023-08-23 15:13:04 +02:00
Marty Oehme
13b5f22953
Fix Path classes wrongly used in string concats 2023-08-23 15:11:58 +02:00
Marty Oehme
f7b0d48c6f
Add info logging to failable functions 2023-08-23 15:10:41 +02:00
Marty Oehme
6cccf00df4
Export TxtTranscription from txt output function 2023-08-23 15:09:53 +02:00
Marty Oehme
f17639a637
Change argument name for transcription 2023-08-23 15:07:48 +02:00
Marty Oehme
30fd269cd9
Reinsert whisper model transcribing 2023-08-23 13:23:19 +02:00
Marty Oehme
3246469be2
Use cuda-enabled base image 
Since I can't build cuda images locally (without nvidia gpu), we make use
of an existing one. That also means downgrading python to Debian's version
3.10.
 2023-08-22 20:35:25 +02:00
Marty Oehme
56fa2eca25
Activate venv without poetry 2023-08-22 20:34:12 +02:00
8 changed files with 530 additions and 197 deletions
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20
Dockerfile
View file

@ -1,4 +1,4 @@
ARG PYTHON_VERSION=3.11.0
ARG PYTHON_VERSION=3.10.0
ARG POETRY_VERSION=1.5.0
FROM python:${PYTHON_VERSION} as staging
@ -26,9 +26,13 @@ ENV PATH="${POETRY_HOME}/bin:${PATH}"
WORKDIR ${APP_PATH}
COPY ./poetry.lock ./pyproject.toml ./README.md ./
WORKDIR ${APP_PATH}
RUN poetry install
# --------------
FROM staging as worker
FROM ghcr.io/pytorch/pytorch-nightly:668af07-cu11.8.0 as worker
ARG APP_NAME=verbanote
ARG APP_PATH=/verbanote
ARG VERBANOTE_OUTPUT_PATH=/out
@ -37,15 +41,19 @@ ENV APP_NAME=${APP_NAME}
ENV APP_PATH=${APP_PATH}
ENV VERBANOTE_OUTPUT_PATH=${VERBANOTE_OUTPUT_PATH}
ENV VERBANOTE_INPUT_PATH=${VERBANOTE_INPUT_PATH}
ENV VIRTUAL_ENV="${APP_PATH}/.venv"
ENV PATH="${APP_PATH}/.venv/bin:${PATH}"
COPY --from=staging ${APP_PATH} ${APP_PATH}
RUN mkdir -p ${VERBANOTE_INPUT_PATH}
RUN mkdir -p ${VERBANOTE_OUTPUT_PATH}
WORKDIR ${APP_PATH}
RUN poetry install
# installing the large models
RUN poetry run ltt install --pytorch-computation-backend=cu118 torch torchvision torchaudio
RUN rm "${VIRTUAL_ENV}/bin/python"
RUN ln -s "/opt/conda/bin/python" "${VIRTUAL_ENV}/bin/python"
RUN which python
RUN ltt install --pytorch-computation-backend=cu117 torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1
COPY ./${APP_NAME} ./${APP_NAME}

2
docker/docker-entrypoint.sh
View file

@ -2,4 +2,4 @@
#
set -e
exec poetry run "$@"
exec "$@"

552
poetry.lock generated
View file

@ -185,6 +185,7 @@ files = [
]
[package.dependencies]
exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
@ -550,6 +551,35 @@ numpy = "*"
[package.extras]
cmawm = ["scipy"]
[[package]]
name = "cmake"
version = "3.27.2"
description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
optional = false
python-versions = "*"
files = [
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{file = "cmake-3.27.2-py2.py3-none-win32.whl", hash = "sha256:53e12deb893da935e236f93accd47dbe2806620cd7654986234dc4487cc49652"},
{file = "cmake-3.27.2-py2.py3-none-win_amd64.whl", hash = "sha256:611f9722c68c40352d38a6c01960ab038c3d0419e7aee3bf18f95b23031e0dfe"},
{file = "cmake-3.27.2-py2.py3-none-win_arm64.whl", hash = "sha256:30620326b51ac2ce0d8f476747af6367a7ea21075c4d065fad9443904b07476a"},
{file = "cmake-3.27.2.tar.gz", hash = "sha256:7cd6e2d7d5a1125f8c26c4f65214f8c942e3f276f98c16cb62ae382c35609f25"},
]
[package.extras]
test = ["coverage (>=4.2)", "flake8 (>=3.0.4)", "path.py (>=11.5.0)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)", "pytest-runner (>=2.9)", "pytest-virtualenv (>=1.7.0)", "scikit-build (>=0.10.0)", "setuptools (>=28.0.0)", "virtualenv (>=15.0.3)", "wheel"]
[[package]]
name = "colorama"
version = "0.4.6"
@ -638,13 +668,13 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
[[package]]
name = "croniter"
version = "1.3.15"
version = "1.4.1"
description = "croniter provides iteration for datetime object with cron like format"
optional = false
python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
files = [
{file = "croniter-1.3.15-py2.py3-none-any.whl", hash = "sha256:f17f877be1d93b9e3191151584a19d8b367b017ab0febc8c5472b9300da61c4c"},
{file = "croniter-1.3.15.tar.gz", hash = "sha256:924a38fda88f675ec6835667e1d32ac37ff0d65509c2152729d16ff205e32a65"},
{file = "croniter-1.4.1-py2.py3-none-any.whl", hash = "sha256:9595da48af37ea06ec3a9f899738f1b2c1c13da3c38cea606ef7cd03ea421128"},
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]
[package.dependencies]
@ -760,6 +790,20 @@ files = [
dnspython = ">=2.0.0"
idna = ">=2.0.0"
[[package]]
name = "exceptiongroup"
version = "1.1.3"
description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
files = [
{file = "exceptiongroup-1.1.3-py3-none-any.whl", hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3"},
{file = "exceptiongroup-1.1.3.tar.gz", hash = "sha256:097acd85d473d75af5bb98e41b61ff7fe35efe6675e4f9370ec6ec5126d160e9"},
]
[package.extras]
test = ["pytest (>=6)"]
[[package]]
name = "fastapi"
version = "0.101.1"
@ -1463,35 +1507,36 @@ pip = ">=22.3,<23.3"
[[package]]
name = "lightning"
version = "2.0.4"
description = "Use Lightning Apps to build everything from production-ready, multi-cloud ML systems to simple research demos."
version = "2.0.7"
description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast."
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
arrow = ">=1.2.0,<3.0"
backoff = ">=2.2.1,<4.0"
beautifulsoup4 = ">=4.8.0,<6.0"
click = "<10.0"
croniter = ">=1.3.0,<1.4.0"
croniter = ">=1.3.0,<1.5.0"
dateutils = "<2.0"
deepdiff = ">=5.7.0,<8.0"
fastapi = ">=0.92.0,<2.0"
fsspec = [
{version = ">=2022.5.0,<2024.0"},
{version = ">=2022.5.0,<2025.0"},
{version = ">2021.06.0,<2025.0", extras = ["http"]},
]
inquirer = ">=2.10.0,<5.0"
Jinja2 = "<5.0"
lightning-cloud = ">=0.5.34"
lightning-cloud = ">=0.5.37"
lightning-utilities = ">=0.7.0,<2.0"
numpy = ">=1.17.2,<3.0"
packaging = ">=17.1,<25.0"
psutil = "<7.0"
pydantic = ">=1.7.4,<4.0"
pydantic = ">=1.7.4,<2.2.0"
python-multipart = ">=0.0.5,<2.0"
pytorch-lightning = "*"
PyYAML = ">=5.4,<8.0"
@ -1507,35 +1552,35 @@ typing-extensions = ">=4.0.0,<6.0"
urllib3 = "<4.0"
uvicorn = "<2.0"
websocket-client = "<3.0"
websockets = "<12.0"
websockets = "<13.0"
[package.extras]
all = ["aiohttp (>=3.8.0,<4.0)", "deepspeed (>=0.8.2,<1.0)", "docker (>=5.0.0,<7.0)", "gym[classic-control] (>=0.17.0,<1.0)", "hydra-core (>=1.0.5,<2.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.18.0,<5.0)", "lightning-api-access (>=0.0.3)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.7.0,<1.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "panel (>=1.0.0,<2.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<5.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "streamlit (>=1.13.0,<2.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<1.0)", "torchvision (>=0.12.0,<1.0)"]
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app-dev = ["aiohttp (>=3.8.0,<4.0)", "coverage (==7.2.7)", "docker (>=5.0.0,<7.0)", "httpx (==0.24.1)", "lightning-api-access (>=0.0.3)", "lightning-fabric (>=1.9.0)", "panel (>=1.0.0,<2.0)", "playwright (==1.36.0)", "psutil (<6.0)", "pympler", "pytest (==7.4.0)", "pytest-asyncio (==0.21.1)", "pytest-cov (==4.1.0)", "pytest-doctestplus (>=0.9.0)", "pytest-rerunfailures (<12.0)", "pytest-timeout (==2.1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<5.0)", "requests-mock (<2.0)", "s3fs (>=2022.5.0,<2024.0)", "setuptools (<69.0)", "streamlit (>=1.13.0,<2.0)", "trio (<0.22.0)"]
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app-ui = ["panel (>=1.0.0,<2.0)", "streamlit (>=1.13.0,<2.0)"]
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name = "requests"
version = "2.31.0"
@ -3628,6 +3813,51 @@ files = [
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]
[[package]]
name = "tiktoken"
version = "0.3.3"
description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
regex = ">=2022.1.18"
requests = ">=2.26.0"
[package.extras]
blobfile = ["blobfile (>=2)"]
[[package]]
name = "tomli"
version = "2.0.1"
@ -3826,6 +4056,43 @@ files = [
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
[[package]]
name = "triton"
version = "2.0.0"
description = "A language and compiler for custom Deep Learning operations"
optional = false
python-versions = "*"
files = [
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]
[package.dependencies]
cmake = "*"
filelock = "*"
lit = "*"
torch = "*"
[package.extras]
tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
tutorials = ["matplotlib", "pandas", "tabulate"]
[[package]]
name = "typer"
version = "0.9.0"
@ -3976,6 +4243,7 @@ h11 = ">=0.8"
httptools = {version = ">=0.5.0", optional = true, markers = "extra == \"standard\""}
python-dotenv = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
pyyaml = {version = ">=5.1", optional = true, markers = "extra == \"standard\""}
typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
uvloop = {version = ">=0.14.0,<0.15.0 || >0.15.0,<0.15.1 || >0.15.1", optional = true, markers = "(sys_platform != \"win32\" and sys_platform != \"cygwin\") and platform_python_implementation != \"PyPy\" and extra == \"standard\""}
watchfiles = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
websockets = {version = ">=10.4", optional = true, markers = "extra == \"standard\""}
@ -4074,17 +4342,17 @@ files = [
[[package]]
name = "websocket-client"
version = "1.6.1"
version = "1.6.2"
description = "WebSocket client for Python with low level API options"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "websocket-client-1.6.1.tar.gz", hash = "sha256:c951af98631d24f8df89ab1019fc365f2227c0892f12fd150e935607c79dd0dd"},
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{file = "websocket-client-1.6.2.tar.gz", hash = "sha256:53e95c826bf800c4c465f50093a8c4ff091c7327023b10bfaff40cf1ef170eaa"},
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]
[package.extras]
docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
optional = ["python-socks", "wsaccel"]
test = ["websockets"]
@ -4256,5 +4524,5 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "abe4e81cd59988f91e16746d9cc05c4d4f49c53957a682d03356ce2270950661"
python-versions = "^3.10"
content-hash = "78450781b9a79630d7889c4c7561068d194f35d1ed5f90455ad2eb4fc4a937a2"

7
pyproject.toml
View file

@ -7,18 +7,15 @@ readme = "README.md"
# packages = [{ include = "verbanote" }]
[tool.poetry.dependencies]
python = "^3.11"
python = "^3.10"
requests = "^2.31.0"
static-ffmpeg = "^2.5"
runpod = "^1.1.3"
pydub = "^0.25.1"
#light-the-torch = "^0.7.5"
light-the-torch = "^0.7.5"
hmmlearn = {git = "https://github.com/hmmlearn/hmmlearn.git"}
pyannote-audio = {git = "https://github.com/pyannote/pyannote-audio.git", rev = "develop"}
#openai-whisper = { git = "https://github.com/openai/whisper.git" }
#hmmlearn = {git = "https://github.com/hmmlearn/hmmlearn.git"}
#pyannote-audio = {git = "https://github.com/pyannote/pyannote-audio.git", rev = "develop"}
openai-whisper = { git = "https://github.com/openai/whisper.git" }
[build-system]
requires = ["poetry-core"]

1
verbanote/file_operations.py
View file

@ -23,6 +23,7 @@ def upload_to_oxo(file: Path, url: str = "https://0x0.st", expires: int = 2) ->
)
if not resp.ok:
raise requests.exceptions.HTTPError()
logging.info(f"Uploaded file {file} to {str(resp.content)}")
return str(resp.content)

13
verbanote/loaders.py
View file

@ -1,11 +1,12 @@
import locale
from pathlib import Path
# from whisper import Whisper
from whisper import Whisper, load_model
from pyannote.audio import Pipeline
import torch
import static_ffmpeg
import file_operations
def prep() -> None:
locale.getpreferredencoding = lambda: "UTF-8"
# download and add ffmpeg to env
@ -26,8 +27,8 @@ def diarization(access_token: str | None) -> Pipeline:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
return pipeline.to(device)
#
# def whisper() -> Whisper:
# # LOAD MODEL INTO VRAM
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# return whisper.load_model("large", device=device)
def whispermodel() -> Whisper:
# LOAD MODEL INTO VRAM
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
return load_model("large", device=device)

99
verbanote/process.py
View file

@ -1,53 +1,97 @@
import logging
import os
import re
import json
from dataclasses import dataclass
from pathlib import Path
from pyannote.audio import Pipeline
from pydub import AudioSegment
# from whisper import Whisper
from whisper import Whisper
MILLISECONDS_TO_SPACE = 2000
@dataclass
class TxtTranscription:
text: str
file: Path
def diarize(audiofile: Path, pipeline: Pipeline, output_path: Path) -> Path:
audiofile_prepended = _add_audio_silence(audiofile)
logging.info(f"Beginning diarization of {audiofile}...")
DIARIZE_FILE = {"uri": "not-important", "audio": audiofile_prepended}
dz = pipeline(DIARIZE_FILE)
out_file = Path.joinpath(output_path, "diarization.txt")
with open(out_file, "w") as text_file:
text_file.write(str(dz))
print("Diarized:")
print(*list(dz.itertracks(yield_label=True))[:10], sep="\n")
logging.info(f"Created diarization in {out_file}.")
return out_file
# def transcribe(
# model: Whisper,
# diarized_groups: list,
# output_path: Path,
# lang: str = "en",
# word_timestamps: bool = True,
# ):
# for i in range(len(diarized_groups)):
# f = {Path.joinpath(output_path, str(i))}
# audio_f = f"{f}.wav"
# json_f = f"{f}.json"
# result = model.transcribe(
# audio=audio_f, language=lang, word_timestamps=word_timestamps
# )
# with open(json_f, "w") as outfile:
# json.dump(result, outfile, indent=4)
def transcribe(
model: Whisper,
diarized_groups: list,
files_path: Path,
lang: str = "en",
word_timestamps: bool = True,
) -> None:
for i in range(len(diarized_groups)):
audio_f = Path.joinpath(files_path, f"{str(i)}.wav")
json_f = Path.joinpath(files_path, f"{str(i)}.json")
logging.info(f"Starting transcription of {str(audio_f)}...")
result = model.transcribe(
audio=str(audio_f), language=lang, word_timestamps=word_timestamps
)
with open(json_f, "w") as outfile:
json.dump(result, outfile, indent=4)
logging.info(f"Transcription written to {str(json_f)}.")
# TODO clean up this mess
def output_txt(diarized_groups: list, transcription_path: Path) -> TxtTranscription:
txt = list("")
gidx = -1
for g in diarized_groups:
shift = re.findall(r"[0-9]+:[0-9]+:[0-9]+\.[0-9]+", string=g[0])[0]
shift = (
_millisec(shift) - MILLISECONDS_TO_SPACE
) # the start time in the original video
shift = max(shift, 0)
gidx += 1
fname = Path.joinpath(transcription_path, f"{str(gidx)}.json")
with open(fname) as f:
captions = json.load(f)["segments"]
logging.info(f"Loaded {fname} for transcription...")
if captions:
speaker = g[0].split()[-1]
txt.append(f"[{speaker}] ")
for c in captions:
txt.append(f"{c['text']}")
txt.append("\n\n")
output = "".join(txt)
fname = Path.joinpath(transcription_path, "transcription_result.txt")
with open(fname, "w", encoding="utf-8") as file:
file.write(output)
logging.info(f"Wrote transcription to output file {fname}.")
return TxtTranscription(text=output, file=fname)
def save_diarized_audio_files(
diarization: Path, audiofile: Path, output_path: Path
) -> list:
groups = _group_speakers(diarization)
_save_individual_audio_files(audiofile, groups, output_path)
_save_individual_audio_files(
audiofile=audiofile, groups=groups, output_path=output_path
)
return groups
@ -56,10 +100,11 @@ def _add_audio_silence(audiofile) -> Path:
spacer = AudioSegment.silent(duration=spacermilli)
audio = AudioSegment.from_wav(audiofile)
audio = spacer.append(audio, crossfade=0)
out_file = Path.joinpath(Path(os.path.dirname(audiofile)), "interview_prepend.wav")
audio.export(out_file, format="wav")
fname = Path.joinpath(Path(os.path.dirname(audiofile)), "interview_prepend.wav")
audio.export(fname, format="wav")
logging.info(f"Exported audiofile with silence prepended to {fname}.")
return out_file
return fname
def _save_individual_audio_files(
@ -73,9 +118,9 @@ def _save_individual_audio_files(
start = _millisec(start) # - spacermilli
end = _millisec(end) # - spacermilli
gidx += 1
audio[start:end].export(
f"{Path.joinpath(output_path, str(gidx))}.wav", format="wav"
)
fname = Path.joinpath(output_path, f"{str(gidx)}.wav")
audio[start:end].export(fname, format="wav")
logging.info(f"Exported audiopart {gidx} of {len(groups)} to {fname}.")
def _group_speakers(diarization_file: Path) -> list:

33
verbanote/rp_handler.py
View file

@ -16,12 +16,15 @@ access_token: str = os.environ.get("VERBANOTE_HF_TOKEN", "")
loaders.prep()
diarize_pipeline = loaders.diarization(access_token)
# whisper_model = loaders.whisper()
# TODO download model into Docker image beforehand
whisper_model = loaders.whispermodel()
def handler(job):
input: dict = job["input"]
url: str | None = input.get("url")
lang: str | None = input.get("lang")
word_timestamps: str | None = input.get("word_timestamps")
if not url:
return {"error": "no file link provided"}
@ -35,17 +38,27 @@ def handler(job):
diarized_groups = process.save_diarized_audio_files(
diarized, audiofile, output_path
)
uploaded_file: str = file_operations.upload_to_oxo(file=diarized, expires=1)
# process.transcribe(
# model=whisper_model, diarized_groups=diarized_groups, output_path=output_path
# )
uploaded_diarization: str = file_operations.upload_to_oxo(file=diarized, expires=1)
process.transcribe(
model=whisper_model,
diarized_groups=diarized_groups,
files_path=output_path,
lang=lang or "en",
word_timestamps=word_timestamps or True,
)
transcription: process.TxtTranscription = process.output_txt(
diarized_groups, output_path
)
uploaded_transcription: str = file_operations.upload_to_oxo(
file=transcription.file, expires=1
)
return {
"speaker_timings": "s3-address-to-speakers",
"transcription_text": "s3-address-to-transcription",
"transcription_page": "web-address-to-deployment",
"audiofile_path": str(audiofile),
"audio_url": uploaded_file,
"audiofile": str(audiofile),
"diarization_url": uploaded_diarization,
"diarization": diarized_groups,
"transcription_url": uploaded_transcription,
"transcription_text": transcription.text,
}