Merge pull request #229 from ahmetoner/add-formatters

Add black and ruff formatters

CHANGELOG.md

@@ -103,9 +103,11 @@ Unreleased
 - Updated default model paths to `~/.cache/whisper` or `/root/.cache/whisper`.
   - For customization, modify the `ASR_MODEL_PATH` environment variable.
   - Ensure Docker volume is set for the corresponding directory to use caching.
+
       ```bash
       docker run -d -p 9000:9000 -e ASR_MODEL_PATH=/data/whisper -v $PWD/yourlocaldir:/data/whisper onerahmet/openai-whisper-asr-webservice:latest
       ```
+
 - Removed the `triton` dependency from `poetry.lock` to ensure the stability of the pipeline for `ARM-based` Docker images
 
 [1.1.1] (2023-05-29)

README.md

@@ -2,16 +2,17 @@
 ![Docker Pulls](https://img.shields.io/docker/pulls/onerahmet/openai-whisper-asr-webservice.svg)
 ![Build](https://img.shields.io/github/actions/workflow/status/ahmetoner/whisper-asr-webservice/docker-publish.yml.svg)
 ![Licence](https://img.shields.io/github/license/ahmetoner/whisper-asr-webservice.svg)
+
 # Whisper ASR Webservice
 
 Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitask model that can perform multilingual speech recognition as well as speech translation and language identification. For more details: [github.com/openai/whisper](https://github.com/openai/whisper/)
 
 ## Features
+
 Current release (v1.4.1) supports following whisper models:
 
 - [openai/whisper](https://github.com/openai/whisper)@[v20231117](https://github.com/openai/whisper/releases/tag/v20231117)
-- [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[v0.10.0](https://github.com/SYSTRAN/faster-whisper/releases/tag/0.10.0)
-
+- [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[v1.0.2](https://github.com/SYSTRAN/faster-whisper/releases/tag/1.0.2)
 
 ## Quick Usage
 
@@ -33,6 +34,7 @@ for more information:
 - [Docker Hub](https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice)
 
 ## Documentation
+
 Explore the documentation by clicking [here](https://ahmetoner.github.io/whisper-asr-webservice).
 
 ## Credits

app/faster_whisper/core.py

 import os
 from io import StringIO
 from threading import Lock
-from typing import Union, BinaryIO
+from typing import BinaryIO, Union
 
 import torch
 import whisper
 from faster_whisper import WhisperModel
 
-from .utils import ResultWriter, WriteTXT, WriteSRT, WriteVTT, WriteTSV, WriteJSON
-
+from .utils import ResultWriter, WriteJSON, WriteSRT, WriteTSV, WriteTXT, WriteVTT
 
 model_name = os.getenv("ASR_MODEL", "base")
 model_path = os.getenv("ASR_MODEL_PATH", os.path.join(os.path.expanduser("~"), ".cache", "whisper"))
@@ -23,14 +22,12 @@ else:
     model_quantization = os.getenv("ASR_QUANTIZATION", "int8")
 
 model = WhisperModel(
-    model_size_or_path=model_name,
-    device=device,
-    compute_type=model_quantization,
-    download_root=model_path
+    model_size_or_path=model_name, device=device, compute_type=model_quantization, download_root=model_path
 )
 
 model_lock = Lock()
 
+
 def transcribe(
     audio,
     task: Union[str, None],
@@ -56,11 +53,7 @@ def transcribe(
         for segment in segment_generator:
             segments.append(segment)
             text = text + segment.text
-        result = {
-            "language": options_dict.get("language", info.language),
-            "segments": segments,
-            "text": text
-        }
+        result = {"language": options_dict.get("language", info.language), "segments": segments, "text": text}
 
     output_file = StringIO()
     write_result(result, output_file, output)
@@ -81,9 +74,7 @@ def language_detection(audio):
     return detected_lang_code
 
 
-def write_result(
-        result: dict, file: BinaryIO, output: Union[str, None]
-):
+def write_result(result: dict, file: BinaryIO, output: Union[str, None]):
     if output == "srt":
         WriteSRT(ResultWriter).write_result(result, file=file)
     elif output == "vtt":
@@ -95,4 +86,4 @@ def write_result(
     elif output == "txt":
         WriteTXT(ResultWriter).write_result(result, file=file)
     else:
-        return 'Please select an output method!'
+        return "Please select an output method!"

app/faster_whisper/utils.py

@@ -69,6 +69,7 @@ class WriteTSV(ResultWriter):
     an environment setting a language encoding that causes the decimal in a floating point number
     to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++.
     """
+
     extension: str = "tsv"
 
     def write_result(self, result: dict, file: TextIO):

app/openai_whisper/core.py

@@ -5,7 +5,7 @@ from typing import BinaryIO, Union
 
 import torch
 import whisper
-from whisper.utils import ResultWriter, WriteTXT, WriteSRT, WriteVTT, WriteTSV, WriteJSON
+from whisper.utils import ResultWriter, WriteJSON, WriteSRT, WriteTSV, WriteTXT, WriteVTT
 
 model_name = os.getenv("ASR_MODEL", "base")
 model_path = os.getenv("ASR_MODEL_PATH", os.path.join(os.path.expanduser("~"), ".cache", "whisper"))
@@ -24,7 +24,7 @@ def transcribe(
     initial_prompt: Union[str, None],
     vad_filter: Union[bool, None],
     word_timestamps: Union[bool, None],
-        output
+    output,
 ):
     options_dict = {"task": task}
     if language:
@@ -58,14 +58,8 @@ def language_detection(audio):
     return detected_lang_code
 
 
-def write_result(
-        result: dict, file: BinaryIO, output: Union[str, None]
-):
-    options = {
-        'max_line_width': 1000,
-        'max_line_count': 10,
-        'highlight_words': False
-    }
+def write_result(result: dict, file: BinaryIO, output: Union[str, None]):
+    options = {"max_line_width": 1000, "max_line_count": 10, "highlight_words": False}
     if output == "srt":
         WriteSRT(ResultWriter).write_result(result, file=file, options=options)
     elif output == "vtt":
@@ -77,4 +71,4 @@ def write_result(
     elif output == "txt":
         WriteTXT(ResultWriter).write_result(result, file=file, options=options)
     else:
-        return 'Please select an output method!'
+        return "Please select an output method!"

app/webservice.py

 import importlib.metadata
 import os
 from os import path
-from typing import BinaryIO, Union, Annotated
+from typing import Annotated, BinaryIO, Union
+from urllib.parse import quote
 
 import ffmpeg
 import numpy as np
-from fastapi import FastAPI, File, UploadFile, Query, applications
+from fastapi import FastAPI, File, Query, UploadFile, applications
 from fastapi.openapi.docs import get_swagger_ui_html
-from fastapi.responses import StreamingResponse, RedirectResponse
+from fastapi.responses import RedirectResponse, StreamingResponse
 from fastapi.staticfiles import StaticFiles
 from whisper import tokenizer
-from urllib.parse import quote
 
 ASR_ENGINE = os.getenv("ASR_ENGINE", "openai_whisper")
 if ASR_ENGINE == "faster_whisper":
-    from .faster_whisper.core import transcribe, language_detection
+    from .faster_whisper.core import language_detection, transcribe
 else:
-    from .openai_whisper.core import transcribe, language_detection
+    from .openai_whisper.core import language_detection, transcribe
 
 SAMPLE_RATE = 16000
-LANGUAGE_CODES = sorted(list(tokenizer.LANGUAGES.keys()))
+LANGUAGE_CODES = sorted(tokenizer.LANGUAGES.keys())
 
-projectMetadata = importlib.metadata.metadata('whisper-asr-webservice')
+projectMetadata = importlib.metadata.metadata("whisper-asr-webservice")
 app = FastAPI(
-    title=projectMetadata['Name'].title().replace('-', ' '),
-    description=projectMetadata['Summary'],
-    version=projectMetadata['Version'],
-    contact={
-        "url": projectMetadata['Home-page']
-    },
+    title=projectMetadata["Name"].title().replace("-", " "),
+    description=projectMetadata["Summary"],
+    version=projectMetadata["Version"],
+    contact={"url": projectMetadata["Home-page"]},
     swagger_ui_parameters={"defaultModelsExpandDepth": -1},
-    license_info={
-        "name": "MIT License",
-        "url": projectMetadata['License']
-    }
+    license_info={"name": "MIT License", "url": projectMetadata["License"]},
 )
 
 assets_path = os.getcwd() + "/swagger-ui-assets"
 if path.exists(assets_path + "/swagger-ui.css") and path.exists(assets_path + "/swagger-ui-bundle.js"):
     app.mount("/assets", StaticFiles(directory=assets_path), name="static")
 
-
     def swagger_monkey_patch(*args, **kwargs):
         return get_swagger_ui_html(
             *args,
@@ -50,7 +44,6 @@ if path.exists(assets_path + "/swagger-ui.css") and path.exists(assets_path + "/
             swagger_js_url="/assets/swagger-ui-bundle.js",
         )
 
-
     applications.get_swagger_ui_html = swagger_monkey_patch
 
 
@@ -61,33 +54,38 @@ async def index():
 
 @app.post("/asr", tags=["Endpoints"])
 async def asr(
-        audio_file: UploadFile = File(...),
+    audio_file: UploadFile = File(...),  # noqa: B008
     encode: bool = Query(default=True, description="Encode audio first through ffmpeg"),
     task: Union[str, None] = Query(default="transcribe", enum=["transcribe", "translate"]),
     language: Union[str, None] = Query(default=None, enum=LANGUAGE_CODES),
     initial_prompt: Union[str, None] = Query(default=None),
-        vad_filter: Annotated[bool | None, Query(
+    vad_filter: Annotated[
+        bool | None,
+        Query(
             description="Enable the voice activity detection (VAD) to filter out parts of the audio without speech",
-                include_in_schema=(True if ASR_ENGINE == "faster_whisper" else False)
-            )] = False,
+            include_in_schema=(True if ASR_ENGINE == "faster_whisper" else False),
+        ),
+    ] = False,
     word_timestamps: bool = Query(default=False, description="Word level timestamps"),
-        output: Union[str, None] = Query(default="txt", enum=["txt", "vtt", "srt", "tsv", "json"])
+    output: Union[str, None] = Query(default="txt", enum=["txt", "vtt", "srt", "tsv", "json"]),
 ):
-    result = transcribe(load_audio(audio_file.file, encode), task, language, initial_prompt, vad_filter, word_timestamps, output)
+    result = transcribe(
+        load_audio(audio_file.file, encode), task, language, initial_prompt, vad_filter, word_timestamps, output
+    )
     return StreamingResponse(
         result,
         media_type="text/plain",
         headers={
-        'Asr-Engine': ASR_ENGINE,
-        'Content-Disposition': f'attachment; filename="{quote(audio_file.filename)}.{output}"'
-    }
+            "Asr-Engine": ASR_ENGINE,
+            "Content-Disposition": f'attachment; filename="{quote(audio_file.filename)}.{output}"',
+        },
     )
 
 
 @app.post("/detect-language", tags=["Endpoints"])
 async def detect_language(
-        audio_file: UploadFile = File(...),
-        encode: bool = Query(default=True, description="Encode audio first through FFmpeg")
+    audio_file: UploadFile = File(...),  # noqa: B008
+    encode: bool = Query(default=True, description="Encode audio first through FFmpeg"),
 ):
     detected_lang_code = language_detection(load_audio(audio_file.file, encode))
     return {"detected_language": tokenizer.LANGUAGES[detected_lang_code], "language_code": detected_lang_code}

docs/build.md

@@ -77,4 +77,3 @@ poetry run gunicorn --bind 0.0.0.0:9000 --workers 1 --timeout 0 app.webservice:a
         ```sh
         docker-compose up --build -f docker-compose.gpu.yml
         ```
-

docs/endpoints.md

@@ -29,7 +29,8 @@ There are 2 endpoints available:
 | encode          | true (default)                                 |
 
 Example request with cURL
-```
+
+```bash
 curl -X POST -H "content-type: multipart/form-data" -F "audio_file=@/path/to/file" 0.0.0.0:9000/asr?output=json
 ```
 

docs/environmental-variables.md

@@ -19,7 +19,6 @@ Available ASR_MODELs are `tiny`, `base`, `small`, `medium`, `large` (only OpenAI
 
 For English-only applications, the `.en` models tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models.
 
-
 ### Configuring the `Model Path`
 
 ```sh

docs/index.md

 Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitask model that can perform multilingual speech recognition as well as speech translation and language identification.
 
 ## Features
+
 Current release (v1.4.1) supports following whisper models:
 
 - [openai/whisper](https://github.com/openai/whisper)@[v20231117](https://github.com/openai/whisper/releases/tag/v20231117)
-- [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[v0.10.0](https://github.com/SYSTRAN/faster-whisper/releases/tag/0.10.0)
+- [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[v1.0.2](https://github.com/SYSTRAN/faster-whisper/releases/tag/1.0.2)
 
 ## Quick Usage
 

docs/run.md

@@ -29,11 +29,12 @@ Docker Hub: <https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice>
     docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base -e ASR_ENGINE=openai_whisper onerahmet/openai-whisper-asr-webservice:latest-gpu
     ```
 
-> Interactive Swagger API documentation is available at http://localhost:9000/docs
+> Interactive Swagger API documentation is available at <http://localhost:9000/docs>
 
 ![Swagger UI](assets/images/swagger-ui.png)
 
 ## Cache
+
 The ASR model is downloaded each time you start the container, using the large model this can take some time.
 If you want to decrease the time it takes to start your container by skipping the download, you can store the cache directory (`~/.cache/whisper` or `/root/.cache/whisper`) to a persistent storage.
 Next time you start your container the ASR Model will be taken from the cache instead of being downloaded again.

pyproject.toml

@@ -4,10 +4,7 @@ version = "1.5.0-dev"
 description = "Whisper ASR Webservice is a general-purpose speech recognition webservice."
 homepage = "https://github.com/ahmetoner/whisper-asr-webservice/"
 license = "https://github.com/ahmetoner/whisper-asr-webservice/blob/main/LICENCE"
-authors = [
-    "Ahmet Öner",
-    "Besim Alibegovic",
-]
+authors = ["Ahmet Öner", "Besim Alibegovic"]
 readme = "README.md"
 packages = [{ include = "app" }]
 
@@ -39,7 +36,47 @@ torch = [
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"
+ruff = "^0.5.0"
+black = "^24.4.2"
+mkdocs = "^1.6.0"
+mkdocs-material = "^9.5.27"
+pymdown-extensions = "^10.8.1"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.black]
+skip-string-normalization = true
+line-length = 120
+
+[tool.ruff]
+# Same as Black.
+line-length = 120
+
+[tool.ruff.lint]
+select = [
+  "E", # pycodestyle errors (settings from FastAPI, thanks, @tiangolo!)
+  "W", # pycodestyle warnings
+  "F", # pyflakes
+  "I", # isort
+  "C", # flake8-comprehensions
+  "B", # flake8-bugbear
+]
+ignore = [
+  "E501", # line too long, handled by black
+  "C901", # too complex
+]
+
+[tool.ruff.lint.isort]
+order-by-type = true
+relative-imports-order = "closest-to-furthest"
+extra-standard-library = ["typing"]
+section-order = [
+  "future",
+  "standard-library",
+  "third-party",
+  "first-party",
+  "local-folder",
+]
+known-first-party = []