84 lines
4.4 KiB
Markdown
84 lines
4.4 KiB
Markdown
# Whisper API server project structure
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The project is a local API service for speech recognition based on the Whisper model. The service is designed as an OpenAI-compatible API, allowing it to be used as a local alternative to cloud-based speech recognition services.
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## Main files
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### Root files
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- **server.py** - Application entry point, initializes and starts the service.
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- **server.sh** - Bash script to start the server with optional conda environment update.
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- **config.json** - Configuration file with service settings.
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- **requirements.txt** - Project dependencies for conda/pip.
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### `app` module
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#### app/\_\_init\_\_.py
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Contains the main class `WhisperServiceAPI`, which initializes the application, loads the configuration, and starts the server on the specified port using the production-ready Waitress server.
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#### app/logger.py
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Configures logging for all application components.
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#### app/transcriber.py
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Contains the `WhisperTranscriber` class, which loads the Whisper model and performs speech recognition. The class determines the optimal device for computations (CPU, CUDA, MPS) and supports acceleration with Flash Attention 2.
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#### app/audio_processor.py
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Contains the `AudioProcessor` class for preprocessing audio files before transcription. Includes methods for:
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- Converting to WAV with a 16 kHz sample rate.
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- Normalizing volume level (with configurable `norm_level` parameters).
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- Applying compression/expansion (with configurable `compand_params` parameters).
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- Speeding up audio playback for faster recognition (with configurable `audio_speed_factor` parameter).
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- Adding silence at the beginning of the recording.
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- Cleaning up temporary files.
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#### app/audio_sources.py
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Contains the abstract class `AudioSource` and its concrete implementations for various audio sources:
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- `UploadedFileSource` - for files uploaded via HTTP request.
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- `URLSource` - for files available via URL.
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- `Base64Source` - for audio encoded in base64.
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- `LocalFileSource` - for local files on the server.
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- `FakeFile` - a helper class for unifying processing from different sources.
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#### app/history_logger.py
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Contains the `HistoryLogger` class for saving transcription history.
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#### app/routes.py
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Contains the classes:
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- `TranscriptionService` - a service for processing and transcribing audio files, including methods for getting audio duration and transcribing from various sources.
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- `Routes` - registers all API endpoints, including OpenAI-compatible routes and an endpoint for retrieving service configuration.
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## Main classes
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### WhisperServiceAPI
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The main application class, initializes the service, loads the configuration, and starts the server using Waitress.
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### WhisperTranscriber
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A class for speech recognition using the Whisper model. Determines the optimal device for computations, loads the model considering available hardware, and performs transcription of audio files.
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### AudioProcessor
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A class for preprocessing audio files. Performs conversion, normalization, and adds silence at the beginning of the recording to improve recognition quality, using configurable parameters.
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### AudioSource (and subclasses)
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An abstract class and its implementations for working with various audio file sources. Provides a unified interface for obtaining audio files from different sources.
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### HistoryLogger
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A class for saving transcription history.
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### TranscriptionService
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A service that combines the logic for processing requests and transcribing audio. Accepts an audio source, processes it, and returns the transcription result.
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### Routes
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A class that registers all API routes of the service, including OpenAI-compatible endpoints for integration with existing systems, as well as an endpoint for retrieving the current configuration.
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## API endpoints
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The service provides several endpoints, including:
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- `/health` - Service status check.
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- `/config` - Get current configuration.
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- `/local/transcriptions` - Transcribe a local file on the server.
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- `/v1/models` - Get a list of available models (OpenAI-compatible).
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- `/v1/audio/transcriptions` - Transcribe an uploaded file (OpenAI-compatible).
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- `/v1/audio/transcriptions/url` - Transcribe from a URL.
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- `/v1/audio/transcriptions/base64` - Transcribe from base64.
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- `/v1/audio/transcriptions/multipart` - Transcribe a file from a multipart form.
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The service is designed to provide maximum flexibility in use and integration with existing systems that support the OpenAI Whisper API. |