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Add local support for audio models (PR #7329) (#7591)

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- Description: run the poetry dependencies
  - Issue: #7329 
  - Dependencies: any dependencies required for this change,
  - Tag maintainer: @rlancemartin

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Comendeiro 2023-08-02 09:24:53 +01:00 committed by GitHub
parent d2adec3818
commit 5c516945d0
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 341 additions and 14 deletions
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35
docs/extras/integrations/document_loaders/youtube_audio.ipynb
View File

@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "e48afb8d",
"metadata": {},
@ -11,7 +12,8 @@
"\n",
"Below we show how to easily go from a YouTube url to text to chat!\n",
"\n",
"We wil use the `OpenAIWhisperParser`, which will use the OpenAI Whisper API to transcribe audio to text.\n",
"We wil use the `OpenAIWhisperParser`, which will use the OpenAI Whisper API to transcribe audio to text, \n",
"and the `OpenAIWhisperParserLocal` for local support and running on private clouds or on premise.\n",
"\n",
"Note: You will need to have an `OPENAI_API_KEY` supplied."
]
@ -24,7 +26,7 @@
"outputs": [],
"source": [
"from langchain.document_loaders.generic import GenericLoader\n",
"from langchain.document_loaders.parsers import OpenAIWhisperParser\n",
"from langchain.document_loaders.parsers import OpenAIWhisperParser, OpenAIWhisperParserLocal\n",
"from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader"
]
},
@ -46,7 +48,8 @@
"outputs": [],
"source": [
"! pip install yt_dlp\n",
"! pip install pydub"
"! pip install pydub\n",
"! pip install librosa"
]
},
{
@ -63,6 +66,18 @@
"Let's take the first lecture of Andrej Karpathy's YouTube course as an example! "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8682f256",
"metadata": {},
"outputs": [],
"source": [
"# set a flag to switch between local and remote parsing\n",
"# change this to True if you want to use local parsing\n",
"local = False"
]
},
{
"cell_type": "code",
"execution_count": 2,
@ -102,7 +117,10 @@
"save_dir = \"~/Downloads/YouTube\"\n",
"\n",
"# Transcribe the videos to text\n",
"loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParser())\n",
"if local:\n",
" loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParserLocal())\n",
"else:\n",
" loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParser())\n",
"docs = loader.load()"
]
},
@ -275,7 +293,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
@ -289,7 +307,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
"version": "3.10.11"
},
"vscode": {
"interpreter": {
"hash": "97cc609b13305c559618ec78a438abc56230b9381f827f22d070313b9a1f3777"
}
}
},
"nbformat": 4,

104
libs/langchain/langchain/document_loaders/parsers/audio.py
View File

@ -73,3 +73,107 @@ class OpenAIWhisperParser(BaseBlobParser):
page_content=transcript.text,
metadata={"source": blob.source, "chunk": split_number},
)
class OpenAIWhisperParserLocal(BaseBlobParser):
"""Transcribe and parse audio files.
Audio transcription is with OpenAI Whisper model locally from transformers
NOTE: By default uses the gpu if available, if you want to use cpu,
please set device = "cpu"
"""
def __init__(self, device: str = "0", lang_model: Optional[str] = None):
try:
from transformers import pipeline
except ImportError:
raise ImportError(
"transformers package not found, please install it with "
"`pip install transformers`"
)
try:
import torch
except ImportError:
raise ImportError(
"torch package not found, please install it with " "`pip install torch`"
)
# set device, cpu by default check if there is a GPU available
if device == "cpu":
self.device = "cpu"
if lang_model is not None:
self.lang_model = lang_model
print("WARNING! Model override. Using model: ", self.lang_model)
else:
# unless overridden, use the small base model on cpu
self.lang_model = "openai/whisper-base"
else:
if torch.cuda.is_available():
self.device = "cuda:0"
# check GPU memory and select automatically the model
mem = torch.cuda.get_device_properties(self.device).total_memory / (
1024**2
)
if mem < 5000:
rec_model = "openai/whisper-base"
elif mem < 7000:
rec_model = "openai/whisper-small"
elif mem < 12000:
rec_model = "openai/whisper-medium"
else:
rec_model = "openai/whisper-large"
# check if model is overridden
if lang_model is not None:
self.lang_model = lang_model
print("WARNING! Model override. Might not fit in your GPU")
else:
self.lang_model = rec_model
else:
"cpu"
print("Using the following model: ", self.lang_model)
# load model for inference
self.pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-medium",
chunk_length_s=30,
device=self.device,
)
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
import io
try:
from pydub import AudioSegment
except ImportError:
raise ValueError(
"pydub package not found, please install it with " "`pip install pydub`"
)
try:
import librosa
except ImportError:
raise ValueError(
"librosa package not found, please install it with "
"`pip install librosa`"
)
# Audio file from disk
audio = AudioSegment.from_file(blob.path)
file_obj = io.BytesIO(audio.export(format="mp3").read())
# Transcribe
print(f"Transcribing part {blob.path}!")
y, sr = librosa.load(file_obj, sr=16000)
prediction = self.pipe(y.copy(), batch_size=8)["text"]
yield Document(
page_content=prediction,
metadata={"source": blob.source},
)

214
libs/langchain/poetry.lock generated
View File

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
# This file is automatically @generated by Poetry and should not be changed by hand.
[[package]]
name = "absl-py"
@ -596,6 +596,17 @@ docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-
tests = ["attrs[tests-no-zope]", "zope-interface"]
tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
[[package]]
name = "audioread"
version = "3.0.0"
description = "multi-library, cross-platform audio decoding"
category = "main"
optional = true
python-versions = ">=3.6"
files = [
{file = "audioread-3.0.0.tar.gz", hash = "sha256:121995bd207eb1fda3d566beb851d3534275925bc35a4fb6da0cb11de0f7251a"},
]
[[package]]
name = "authlib"
version = "1.2.0"
@ -4652,7 +4663,6 @@ optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
files = [
{file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
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]
[[package]]
@ -5063,6 +5073,22 @@ atomic-cache = ["atomicwrites"]
nearley = ["js2py"]
regex = ["regex"]
[[package]]
name = "lazy-loader"
version = "0.3"
description = "lazy_loader"
category = "main"
optional = true
python-versions = ">=3.7"
files = [
{file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
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[package.extras]
lint = ["pre-commit (>=3.3)"]
test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
[[package]]
name = "libclang"
version = "16.0.0"
@ -5114,6 +5140,38 @@ files = [
[package.dependencies]
numpy = "*"
[[package]]
name = "librosa"
version = "0.10.0.post2"
description = "Python module for audio and music processing"
category = "main"
optional = true
python-versions = ">=3.7"
files = [
{file = "librosa-0.10.0.post2-py3-none-any.whl", hash = "sha256:0f3b56118cb01ea89df4b04e924c7f48c5c13d42cc55a12540eb04ae87ab5848"},
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]
[package.dependencies]
audioread = ">=2.1.9"
decorator = ">=4.3.0"
joblib = ">=0.14"
lazy-loader = ">=0.1"
msgpack = ">=1.0"
numba = ">=0.51.0"
numpy = ">=1.20.3,<1.22.0 || >1.22.0,<1.22.1 || >1.22.1,<1.22.2 || >1.22.2"
pooch = ">=1.0,<1.7"
scikit-learn = ">=0.20.0"
scipy = ">=1.2.0"
soundfile = ">=0.12.1"
soxr = ">=0.3.2"
typing-extensions = ">=4.1.1"
[package.extras]
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tests = ["matplotlib (>=3.3.0)", "packaging (>=20.0)", "pytest", "pytest-cov", "pytest-mpl", "resampy (>=0.2.2)", "samplerate", "types-decorator"]
[[package]]
name = "linkify-it-py"
version = "2.0.2"
@ -5135,6 +5193,40 @@ dev = ["black", "flake8", "isort", "pre-commit", "pyproject-flake8"]
doc = ["myst-parser", "sphinx", "sphinx-book-theme"]
test = ["coverage", "pytest", "pytest-cov"]
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importlib-metadata = {version = "*", markers = "python_version < \"3.9\""}
llvmlite = ">=0.40.0dev0,<0.41"
numpy = ">=1.21,<1.25"
[[package]]
name = "numcodecs"
version = "0.11.0"
@ -7770,6 +7901,28 @@ files = [
dev = ["pre-commit", "tox"]
testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "pooch"
version = "1.6.0"
description = "\"Pooch manages your Python library's sample data files: it automatically downloads and stores them in a local directory, with support for versioning and corruption checks.\""
category = "main"
optional = true
python-versions = ">=3.6"
files = [
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appdirs = ">=1.3.0"
packaging = ">=20.0"
requests = ">=2.19.0"
[package.extras]
progress = ["tqdm (>=4.41.0,<5.0.0)"]
sftp = ["paramiko (>=2.7.0)"]
xxhash = ["xxhash (>=1.4.3)"]
[[package]]
name = "portalocker"
version = "2.7.0"
@ -10576,6 +10729,51 @@ files = [
{file = "soupsieve-2.4.1.tar.gz", hash = "sha256:89d12b2d5dfcd2c9e8c22326da9d9aa9cb3dfab0a83a024f05704076ee8d35ea"},
]
[[package]]
name = "soxr"
version = "0.3.5"
description = "High quality, one-dimensional sample-rate conversion library"
category = "main"
optional = true
python-versions = ">=3.6"
files = [
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{file = "soxr-0.3.5.tar.gz", hash = "sha256:b6b60f6381c98249a2f2a594e9234b647b78856c76c060597d53ed27b6efd249"},
]
[package.dependencies]
numpy = "*"
[package.extras]
docs = ["linkify-it-py", "myst-parser", "sphinx", "sphinx-book-theme"]
test = ["pytest"]
[[package]]
name = "spacy"
version = "3.5.3"
@ -11752,7 +11950,7 @@ files = [
]
[package.dependencies]
accelerate = {version = ">=0.20.2", optional = true, markers = "extra == \"accelerate\" or extra == \"torch\""}
accelerate = {version = ">=0.20.2", optional = true, markers = "extra == \"accelerate\""}
filelock = "*"
huggingface-hub = ">=0.14.1,<1.0"
numpy = ">=1.17"
@ -13224,15 +13422,15 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
cffi = ["cffi (>=1.11)"]
[extras]
all = ["O365", "aleph-alpha-client", "amadeus", "anthropic", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "langkit", "lark", "libdeeplake", "lxml", "manifest-ml", "marqo", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "octoai-sdk", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "python-arango", "pyvespa", "qdrant-client", "rdflib", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "spacy", "steamship", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha", "xinference"]
azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"]
all = ["anthropic", "clarifai", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "marqo", "pymongo", "weaviate-client", "redis", "google-api-python-client", "google-auth", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "libdeeplake", "pgvector", "psycopg2-binary", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect", "azure-cosmos", "lancedb", "langkit", "lark", "pexpect", "pyvespa", "O365", "jq", "docarray", "steamship", "pdfminer-six", "lxml", "requests-toolbelt", "neo4j", "openlm", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "momento", "singlestoredb", "tigrisdb", "nebula3-python", "awadb", "esprima", "octoai-sdk", "rdflib", "amadeus", "xinference", "librosa", "python-arango"]
azure = ["azure-identity", "azure-cosmos", "openai", "azure-core", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-search-documents"]
clarifai = ["clarifai"]
cohere = ["cohere"]
docarray = ["docarray"]
embeddings = ["sentence-transformers"]
extended-testing = ["atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "mwparserfromhell", "mwxml", "openai", "openai", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "requests-toolbelt", "scikit-learn", "streamlit", "sympy", "telethon", "tqdm", "xinference", "zep-python"]
extended-testing = ["beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "jq", "pdfminer-six", "pgvector", "pypdf", "pymupdf", "pypdfium2", "tqdm", "lxml", "atlassian-python-api", "mwparserfromhell", "mwxml", "pandas", "telethon", "psychicapi", "zep-python", "gql", "requests-toolbelt", "html2text", "py-trello", "scikit-learn", "streamlit", "pyspark", "openai", "sympy", "rapidfuzz", "openai", "rank-bm25", "geopandas", "jinja2", "xinference", "gitpython"]
javascript = ["esprima"]
llms = ["anthropic", "clarifai", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openllm", "openlm", "torch", "transformers", "xinference"]
llms = ["anthropic", "clarifai", "cohere", "openai", "openllm", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers", "xinference"]
openai = ["openai", "tiktoken"]
qdrant = ["qdrant-client"]
text-helpers = ["chardet"]
@ -13240,4 +13438,4 @@ text-helpers = ["chardet"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "ef2b1d30e0fa872ce764c8a4cbc6e0a460bc9391a6465ee29d657e83b5459391"
content-hash = "d31f0cfa520c75d9342b58db50296fdc4833b7f6d5895660ae47c26d6e5758ac"

2
libs/langchain/pyproject.toml
View File

@ -127,6 +127,7 @@ geopandas = {version = "^0.13.1", optional = true}
xinference = {version = "^0.0.6", optional = true}
python-arango = {version = "^7.5.9", optional = true}
gitpython = {version = "^3.1.32", optional = true}
librosa = {version="^0.10.0.post2", optional = true }
[tool.poetry.group.test.dependencies]
# The only dependencies that should be added are
@ -318,6 +319,7 @@ all = [
"rdflib",
"amadeus",
"xinference",
"librosa",
"python-arango",
]