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Reinsert diarization dependencies

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This commit is contained in:
Marty Oehme 2023-08-22 14:27:52 +02:00
parent 48095a1dc9
commit 64123a29e0
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
8 changed files with 105 additions and 399 deletions
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@ -31,18 +31,21 @@ COPY ./poetry.lock ./pyproject.toml ./README.md ./
FROM staging as worker FROM staging as worker
ARG APP_NAME=verbanote ARG APP_NAME=verbanote
ARG APP_PATH=/verbanote ARG APP_PATH=/verbanote
ARG VERBANOTE_OUTPUT_PATH=/transcriptions ARG VERBANOTE_OUTPUT_PATH=/out
ARG VERBANOTE_INPUT_PATH=/in
ENV APP_NAME=${APP_NAME} ENV APP_NAME=${APP_NAME}
ENV APP_PATH=${APP_PATH} ENV APP_PATH=${APP_PATH}
ENV VERBANOTE_OUTPUT_PATH=${VERBANOTE_OUTPUT_PATH} ENV VERBANOTE_OUTPUT_PATH=${VERBANOTE_OUTPUT_PATH}
ENV VERBANOTE_INPUT_PATH=${VERBANOTE_INPUT_PATH}
RUN mkdir -p ${VERBANOTE_INPUT_PATH}
RUN mkdir -p ${VERBANOTE_OUTPUT_PATH} RUN mkdir -p ${VERBANOTE_OUTPUT_PATH}
WORKDIR ${APP_PATH} WORKDIR ${APP_PATH}
RUN poetry install RUN poetry install
# installing the large models # installing the large models
# RUN poetry run ltt install --pytorch-computation-backend=cu118 torch torchvision torchaudio RUN poetry run ltt install --pytorch-computation-backend=cu118 torch torchvision torchaudio
COPY ./${APP_NAME} ./${APP_NAME} COPY ./${APP_NAME} ./${APP_NAME}

381
poetry.lock generated
View file

@ -312,17 +312,17 @@ wcwidth = ">=0.1.4"
[[package]] [[package]]
name = "boto3" name = "boto3"
version = "1.28.30" version = "1.28.31"
description = "The AWS SDK for Python" description = "The AWS SDK for Python"
optional = false optional = false
python-versions = ">= 3.7" python-versions = ">= 3.7"
files = [ files = [
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] ]
[package.dependencies] [package.dependencies]
botocore = ">=1.31.30,<1.32.0" botocore = ">=1.31.31,<1.32.0"
jmespath = ">=0.7.1,<2.0.0" jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.6.0,<0.7.0" s3transfer = ">=0.6.0,<0.7.0"
@ -331,13 +331,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]] [[package]]
name = "botocore" name = "botocore"
version = "1.31.30" version = "1.31.31"
description = "Low-level, data-driven core of boto 3." description = "Low-level, data-driven core of boto 3."
optional = false optional = false
python-versions = ">= 3.7" python-versions = ">= 3.7"
files = [ files = [
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] ]
[package.dependencies] [package.dependencies]
@ -550,35 +550,6 @@ numpy = "*"
[package.extras] [package.extras]
cmawm = ["scipy"] 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 = "*"
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[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]] [[package]]
name = "colorama" name = "colorama"
version = "0.4.6" version = "0.4.6"
@ -836,45 +807,45 @@ testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "p
[[package]] [[package]]
name = "fonttools" name = "fonttools"
version = "4.42.0" version = "4.42.1"
description = "Tools to manipulate font files" description = "Tools to manipulate font files"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
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lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.11" python-versions = "^3.11"
content-hash = "a978218afe88e07486717a7841980f9826e1d5ae2b5238dbe361db3d5cab81b1" content-hash = "abe4e81cd59988f91e16746d9cc05c4d4f49c53957a682d03356ce2270950661"

3
pyproject.toml
View file

@ -13,6 +13,9 @@ static-ffmpeg = "^2.5"
runpod = "^1.1.3" runpod = "^1.1.3"
pydub = "^0.25.1" pydub = "^0.25.1"
#light-the-torch = "^0.7.5" #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" } #openai-whisper = { git = "https://github.com/openai/whisper.git" }
#hmmlearn = {git = "https://github.com/hmmlearn/hmmlearn.git"} #hmmlearn = {git = "https://github.com/hmmlearn/hmmlearn.git"}
#pyannote-audio = {git = "https://github.com/pyannote/pyannote-audio.git", rev = "develop"} #pyannote-audio = {git = "https://github.com/pyannote/pyannote-audio.git", rev = "develop"}

4
verbanote/file_operations.py
View file

@ -1,4 +1,5 @@
from pathlib import Path from pathlib import Path
import logging
import requests import requests
import subprocess import subprocess
@ -6,11 +7,13 @@ import subprocess
def download_from_url(url: str, input_path: Path) -> Path: def download_from_url(url: str, input_path: Path) -> Path:
resp = requests.get(url) resp = requests.get(url)
if not resp.ok: if not resp.ok:
logging.error(f"Created error code: {resp.status_code}")
raise requests.exceptions.HTTPError() raise requests.exceptions.HTTPError()
# TODO think about implementing a naming scheme based on url path # TODO think about implementing a naming scheme based on url path
fname = Path.joinpath(input_path, "inputfile") fname = Path.joinpath(input_path, "inputfile")
with open(fname, mode="wb") as file: with open(fname, mode="wb") as file:
file.write(resp.content) file.write(resp.content)
logging.info(f"Downloaded input file: {fname}")
return fname return fname
@ -41,4 +44,5 @@ def convert_to_wav(file: Path, output_path: Path) -> Path:
fn, fn,
] ]
) )
logging.info(f"Converted {file} to wav format: {fn}")
return fn return fn

20
verbanote/loaders.py
View file

@ -1,8 +1,8 @@
import locale import locale
from pathlib import Path from pathlib import Path
# from whisper import Whisper # from whisper import Whisper
# from pyannote.audio import Pipeline from pyannote.audio import Pipeline
# import torch import torch
import static_ffmpeg import static_ffmpeg
import file_operations import file_operations
@ -18,14 +18,14 @@ def audiofile(url: str, input_path: Path) -> Path:
file.unlink() file.unlink()
return file_wav return file_wav
#
# def diarization(access_token: str | None) -> Pipeline: def diarization(access_token: str | None) -> Pipeline:
# pipeline = Pipeline.from_pretrained( pipeline = Pipeline.from_pretrained(
# "pyannote/speaker-diarization", use_auth_token=access_token "pyannote/speaker-diarization", use_auth_token=access_token
# ) )
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# return pipeline.to(device) return pipeline.to(device)
#
# #
# def whisper() -> Whisper: # def whisper() -> Whisper:
# # LOAD MODEL INTO VRAM # # LOAD MODEL INTO VRAM

24
verbanote/notebook.py
View file

@ -1,24 +0,0 @@
## SETTINGS FOR LATER
from pathlib import Path
# @markdown Enter the URL of the YouTube video, or the path to the video/audio file you want to transcribe, give the output path, etc. and run the cell. HTML file embeds the video for YouTube, and audio for media files.
Source = "Youtube" # @param ['Youtube', 'File (Google Drive)']
# @markdown ---
# @markdown #### **Youtube video**
video_url = "https://youtu.be/hpZFJctBUHQ" # @param {type:"string"}
# store_audio = True #@param {type:"boolean"}
# @markdown ---
# @markdown #### **Google Drive video or audio path (mp4, wav, mp3)**
video_path = "/content/drive/MyDrive/Customer_Service.mp3" # @param {type:"string"}
# @markdown ---
output_path = "/content/transcript/" # @param {type:"string"}
output_path = str(Path(output_path))
# @markdown ---
# @markdown #### **Title for transcription of media file**
audio_title = "Sample Order Taking" # @param {type:"string"}
# @markdown ---
# @markdown #### Copy a token from your [Hugging Face tokens page](https://huggingface.co/settings/tokens) and paste it below.
access_token = "hf_" # @param {type:"string"}
# @markdown ---
# @markdown **Run this cell again if you change the video.**

38
verbanote/process.py
View file

@ -4,7 +4,7 @@ import json
from pathlib import Path from pathlib import Path
from pyannote.audio import Pipeline from pyannote.audio import Pipeline
from pydub import AudioSegment from pydub import AudioSegment
from whisper import Whisper # from whisper import Whisper
MILLISECONDS_TO_SPACE = 2000 MILLISECONDS_TO_SPACE = 2000
@ -12,8 +12,8 @@ MILLISECONDS_TO_SPACE = 2000
def diarize(audiofile: Path, pipeline: Pipeline, output_path: Path) -> Path: def diarize(audiofile: Path, pipeline: Pipeline, output_path: Path) -> Path:
audiofile_prepended = _add_audio_silence(audiofile) audiofile_prepended = _add_audio_silence(audiofile)
DEMO_FILE = {"uri": "blabla", "audio": audiofile_prepended} DIARIZE_FILE = {"uri": "not-important", "audio": audiofile_prepended}
dz = pipeline(DEMO_FILE) dz = pipeline(DIARIZE_FILE)
out_file = Path.joinpath(output_path, "diarization.txt") out_file = Path.joinpath(output_path, "diarization.txt")
with open(out_file, "w") as text_file: with open(out_file, "w") as text_file:
@ -25,22 +25,22 @@ def diarize(audiofile: Path, pipeline: Pipeline, output_path: Path) -> Path:
return out_file return out_file
def transcribe( # def transcribe(
model: Whisper, # model: Whisper,
diarized_groups: list, # diarized_groups: list,
output_path: Path, # output_path: Path,
lang: str = "en", # lang: str = "en",
word_timestamps: bool = True, # word_timestamps: bool = True,
): # ):
for i in range(len(diarized_groups)): # for i in range(len(diarized_groups)):
f = {Path.joinpath(output_path, str(i))} # f = {Path.joinpath(output_path, str(i))}
audio_f = f"{f}.wav" # audio_f = f"{f}.wav"
json_f = f"{f}.json" # json_f = f"{f}.json"
result = model.transcribe( # result = model.transcribe(
audio=audio_f, language=lang, word_timestamps=word_timestamps # audio=audio_f, language=lang, word_timestamps=word_timestamps
) # )
with open(json_f, "w") as outfile: # with open(json_f, "w") as outfile:
json.dump(result, outfile, indent=4) # json.dump(result, outfile, indent=4)
def save_diarized_audio_files( def save_diarized_audio_files(

25
verbanote/rp_handler.py
View file

@ -1,17 +1,21 @@
import logging
from pathlib import Path from pathlib import Path
import runpod import runpod
from runpod.serverless import os from runpod.serverless import os
import loaders import loaders
# import process import file_operations
import process
output_path:Path = Path(os.environ.get("VERBANOTE_OUTPUT_PATH", "/in")) logging.basicConfig(level=logging.DEBUG)
input_path:Path = Path(os.environ.get("VERBANOTE_INPUT_PATH", "/out"))
input_path: Path = Path(os.environ.get("VERBANOTE_INPUT_PATH", "/in"))
output_path: Path = Path(os.environ.get("VERBANOTE_OUTPUT_PATH", "/out"))
access_token: str = os.environ.get("VERBANOTE_HF_TOKEN", "") access_token: str = os.environ.get("VERBANOTE_HF_TOKEN", "")
loaders.prep() loaders.prep()
# diarize_pipeline = loaders.diarization(access_token) diarize_pipeline = loaders.diarization(access_token)
# whisper_model = loaders.whisper() # whisper_model = loaders.whisper()
@ -27,10 +31,11 @@ def handler(job):
except Exception: except Exception:
return {"error": "audiofile import failed"} return {"error": "audiofile import failed"}
# diarized = process.diarize(audiofile, diarize_pipeline, output_path) diarized = process.diarize(audiofile, diarize_pipeline, output_path)
# diarized_groups = process.save_diarized_audio_files( diarized_groups = process.save_diarized_audio_files(
# diarized, audiofile, output_path diarized, audiofile, output_path
# ) )
uploaded_file: str = file_operations.upload_to_oxo(file=diarized, expires=1)
# process.transcribe( # process.transcribe(
# model=whisper_model, diarized_groups=diarized_groups, output_path=output_path # model=whisper_model, diarized_groups=diarized_groups, output_path=output_path
# ) # )
@ -39,9 +44,11 @@ def handler(job):
"speaker_timings": "s3-address-to-speakers", "speaker_timings": "s3-address-to-speakers",
"transcription_text": "s3-address-to-transcription", "transcription_text": "s3-address-to-transcription",
"transcription_page": "web-address-to-deployment", "transcription_page": "web-address-to-deployment",
"audiofile_path": str(audiofile) "audiofile_path": str(audiofile),
"audio_url": uploaded_file,
} }
# speakers = { # speakers = {
# # speaker, textboxcolor, speaker color # # speaker, textboxcolor, speaker color
# "SPEAKER_00": ("SPEAKER00", "white", "darkgreen"), # "SPEAKER_00": ("SPEAKER00", "white", "darkgreen"),