62 lines
2.5 KiB
Markdown
62 lines
2.5 KiB
Markdown
# Whisper Speech-to-Text API Service
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## Overview
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This project is a lightweight, OpenAI-compatible API server for transcribing audio to text using the Whisper model. It's designed to run locally, making it easy to set up and use for speech-to-text tasks.
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## Features
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- **OpenAI API Compatibility**: Fully compatible with OpenAI's `/v1/audio/transcriptions` and `/v1/models` endpoints.
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- **Local File Support**: Transcribe audio files stored locally on your machine.
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- **Multiple Input Methods**: Supports:
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- Local file paths
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- Files accessible via URL
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- Base64-encoded audio
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- Multipart form uploads
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- **Easy Setup**: Designed to run as a local service with minimal configuration.
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- **Hardware Optimization**: Utilizes GPU (CUDA, MPS) or CPU for efficient processing.
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- **Health Check**: Includes a `/health` endpoint for service monitoring.
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## Recommended Model
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For Russian language transcription, we recommend using the [**whisper-large-v3-russian**](https://huggingface.co/antony66/whisper-large-v3-russian) model from Hugging Face. This model is fine-tuned specifically for Russian speech recognition and delivers high accuracy. For faster transcription with slightly lower accuracy, consider the [**whisper-large-v3-turbo-russian**](https://huggingface.co/dvislobokov/whisper-large-v3-turbo-russian) model, which is optimized for speed.
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Perfect for local development or offline use cases where OpenAI's API isn't accessible.
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## Quick Start
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1. **Edit the Configuration File (`config.json`)**:
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- Set the path to your Whisper model (`model_path`).
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- Configure other parameters like language (`language`), chunk size (`chunk_length_s`), batch size (`batch_size`), and audio normalization settings.
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```json
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{
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"service_port": 5042,
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"model_path": "/path/to/your/whisper-model",
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"language": "english",
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"chunk_length_s": 30,
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"batch_size": 16,
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"max_new_tokens": 256,
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"return_timestamps": false,
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"norm_level": "-0.5",
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"compand_params": "0.3,1 -90,-90,-70,-70,-60,-20,0,0 -5 0 0.2"
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}
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```
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2. **Run the Server**:
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- Simply execute the `server.sh` script:
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```bash
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./server.sh
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```
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- If you need to update the environment, use:
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```bash
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./server.sh --update
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```
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3. **Use the API**:
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- Once the server is running, you can send transcription requests.
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- Example request (curl):
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```bash
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curl -X POST -F file=@audio.mp3 http://localhost:5042/v1/audio/transcriptions | jq -r '.text'
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```
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Enjoy seamless audio-to-text transcription with your local Whisper API server! |