Merge branch 'master' into Whisper-Based

This commit is contained in:
2025-08-17 23:28:08 +09:00
committed by GitHub
6 changed files with 152 additions and 24 deletions

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@@ -5,7 +5,7 @@ WORKDIR /app
RUN apt-get update && apt-get install -y \
ffmpeg \
python3-pip \
&& rm -rf /var/lib/apt/lists/*
python3-venv \
COPY requirements.txt .
RUN pip install --no-cache-dir --default-timeout=100 -r requirements.txt

21
LICENSE Normal file
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@@ -0,0 +1,21 @@
MIT License
Copyright (c) 2025 Vladimirov Vladislav
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

104
README.md Normal file
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@@ -0,0 +1,104 @@
BASED ON https://github.com/salute-developers/GigaAM
# Simple ASR Server
This project provides a RESTful API for audio transcription using a Whisper model. The API is built with FastAPI and runs in a Docker container.
## Prerequisites
Before you begin, ensure you have the following installed:
* [Docker](https://docs.docker.com/get-docker/)
* [Docker Compose](https://docs.docker.com/compose/install/)
## Project Structure
```
.
├── app.py # Main application file with FastAPI endpoint
├── docker-compose.yml # Docker Compose configuration
├── Dockerfile # Dockerfile for building the application image
├── model/ # Directory for Whisper model files
└── requirements.txt # Python dependencies
```
## Setup
1. **Clone the repository:**
```bash
git clone https://github.com/SlavaVlad/simple-asr-server
cd simple-asr-server
```
3. **Add API keys:**
Create a `keys.txt` file in the root of the project and add your API keys, one per line.
## Building and Running the Project
You can build and run the project using Docker Compose.
1. **Build the Docker image:**
```bash
docker-compose build
```
2. **Run the container:**
```bash
docker-compose up
```
The application will be available at `http://0.0.0.0:9854`.
## API Endpoint
### POST /transcribe
This endpoint accepts an audio file and returns the transcription.
* **URL:** `/transcribe`
* **Method:** `POST`
* **Headers:**
* `X-API-Key`: Your API key.
* **Form Data:**
* `file`: The audio file to be transcribed.
**Example using `curl`:**
```bash
curl -X POST "http://localhost:9854/transcribe" \
-H "X-API-Key: YOUR_API_KEY" \
-F "file=@/path/to/your/audio.wav"
```
**Successful Response (200 OK):**
```json
{
"transcription": [
{
"start_time": 0.0,
"end_time": 2.5,
"transcription": "Hello world."
}
],
"text": "Hello world. ",
"metrics": {
"processing_time": 5.2,
"rtf": 0.5,
"word_rate": 2.0
}
}
```
**Error Response (401 Unauthorized):**
If the API key is missing or invalid.
```json
{
"detail": "Invalid API Key"
}
```

37
app.py
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@@ -2,10 +2,10 @@ import logging
import os
import subprocess
import time
from os import getenv
from typing import Dict
from typing import Optional, Union, List, Tuple
import whisper
import gigaam
from fastapi import FastAPI, Depends, HTTPException, UploadFile, File
from fastapi.security import APIKeyHeader
@@ -126,21 +126,26 @@ async def transcribe_audio(
# Transcribe
logger.info("Starting transcription")
result = model.transcribe(
temp_output_path,
verbose=verbose,
temperature=temperature,
compression_ratio_threshold=compression_ratio_threshold,
logprob_threshold=logprob_threshold,
no_speech_threshold=no_speech_threshold,
condition_on_previous_text=condition_on_previous_text,
initial_prompt=initial_prompt,
word_timestamps=word_timestamps,
prepend_punctuations=prepend_punctuations,
append_punctuations=append_punctuations,
clip_timestamps=clip_timestamps,
hallucination_silence_threshold=hallucination_silence_threshold
if original_duration > 30:
logger.info("Audio duration > 30 seconds, using transcribe_longform")
transcription_result = model.transcribe_longform(
temp_output_path
)
else:
logger.info("Audio duration <= 30 seconds, using transcribe")
transcription_result = model.transcribe(
temp_output_path
)
full_text = ""
for part in transcription_result:
if part["transcription"].strip() != "":
full_text += part["transcription"].strip() + " "
result = {
"transcription": transcription_result,
"text": full_text
}
return result

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@@ -3,8 +3,4 @@ services:
build: .
ports:
- "9854:9854"
devices:
- "/dev/kfd:/dev/kfd"
- "/dev/dri:/dev/dri"
group_add:
- video
command: ["python", "app.py"]

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@@ -1,6 +1,8 @@
fastapi
uvicorn[standard]
python-multipart
openai-whisper
gigaam
gigaam[longform]
ffmpeg-python
PyYAML
numpy<2.0.0