api: add support OpenAI REST transcription api

README.md

@@ -45,6 +45,22 @@ source whisper_env/bin/activate
 ```
 
 
+### OpenAI REST interface
+
+#### Server
+
+```bash
+python3 run_server.py --port 9090 --backend faster_whisper --max_clients 4 --max_connection_time 600 --enable_rest --cors-origins="http://localhost:8080,http://127.0.0.1:8080"
+```
+
+#### Client
+
+```bash
+python3 client_openai.py $AUDIO_FILE
+```
+
+
+
 ### Setting up NVIDIA/TensorRT-LLM for TensorRT backend
 - Please follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) for setup of [NVIDIA/TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) and for building Whisper-TensorRT engine.
 

client_openai.py

@@ -0,0 +1,38 @@
+import sys
+import requests
+
+if len(sys.argv) < 2:
+    print("Usage: python transcribe_file.py <path_to_audio_file>")
+    sys.exit(1)
+
+audio_file = sys.argv[1]
+
+# Configuration
+host = "localhost"
+port = 8000  # Default REST port; change if you used --rest_port
+url = f"http://{host}:{port}/v1/audio/transcriptions"
+model = "small"  # Or "whisper-1" (mapped to small internally)
+language = "en"  # Or "hi" for Hindi
+response_format = "json"  # Options: "json", "text", "verbose_json", "srt", "vtt"
+
+# Prepare the request
+files = {"file": open(audio_file, "rb")}
+data = {
+    "model": model,
+    "language": language,
+    "response_format": response_format,
+    # Optional: Add "prompt" for style guidance, "temperature" (0-1), etc.
+}
+
+# Send the request
+response = requests.post(url, files=files, data=data)
+
+if response.status_code == 200:
+    if response_format == "json" or response_format == "verbose_json":
+        result = response.json()
+        print("Transcript:", result.get("text", "No text found"))
+        # If you need translation, post-process here (e.g., using another API like Google Translate)
+    else:
+        print("Transcript:", response.text)
+else:
+    print("Error:", response.status_code, response.json().get("error", "Unknown error"))

requirements/server.txt

@@ -20,4 +20,8 @@ openvino
 openvino-genai
 openvino-tokenizers
 optimum 
-optimum-intel
+optimum-intel
+
+fastapi
+uvicorn
+python-multipart

run_server.py

@@ -1,5 +1,14 @@
 import argparse
 import os
+import threading
+import logging
+from fastapi import FastAPI
+from fastapi import UploadFile, Form
+import uvicorn
+import tempfile
+import shutil
+import json
+from starlette.responses import PlainTextResponse, JSONResponse
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -43,6 +52,20 @@ if __name__ == "__main__":
                         type=str,
                         default="~/.cache/whisper-live/",
                         help='Path to cache the converted ctranslate2 models.')
+    parser.add_argument(
+        "--rest_port", type=int, default=8000, help="Port for the REST API server."
+    )
+    parser.add_argument(
+        "--enable_rest",
+        action="store_true",
+        help="Enable the OpenAI-compatible REST API endpoint.",
+    )
+    parser.add_argument(
+        '--cors-origins',
+        type=str,
+        default=None,
+        help="Comma-separated list of allowed CORS origins (e.g., 'http://localhost:3000,http://example.com'). Defaults to localhost/127.0.0.1 on the WebSocket port."
+    )
     args = parser.parse_args()
 
     if args.backend == "tensorrt":
@@ -65,5 +88,8 @@ if __name__ == "__main__":
         single_model=not args.no_single_model,
         max_clients=args.max_clients,
         max_connection_time=args.max_connection_time,
-        cache_path=args.cache_path
-    )
+        cache_path=args.cache_path,
+        rest_port=args.rest_port,
+        enable_rest=args.enable_rest,
+        cors_origins=args.cors_origins,
+    )

whisper_live/server.py

@@ -5,9 +5,18 @@ import queue
 import json
 import functools
 import logging
+import shutil
+import tempfile
+from typing import Optional, List
+from fastapi import FastAPI, UploadFile, Form
+from fastapi.middleware.cors import CORSMiddleware
+from starlette.responses import PlainTextResponse, JSONResponse
+import uvicorn
+from faster_whisper import WhisperModel
+import torch
+
 from enum import Enum
 from typing import List, Optional
-
 import numpy as np
 from websockets.sync.server import serve
 from websockets.exceptions import ConnectionClosed
@@ -403,7 +412,10 @@ class TranscriptionServer:
             single_model=False,
             max_clients=4,
             max_connection_time=600,
-            cache_path="~/.cache/whisper-live/"):
+            cache_path="~/.cache/whisper-live/",
+            rest_port=8000,
+            enable_rest=False,
+            cors_origins: Optional[str] = None):
         """
         Run the transcription server.
 
@@ -427,6 +439,122 @@ class TranscriptionServer:
                 logging.info("Single model mode currently only works with custom models.")
         if not BackendType.is_valid(backend):
             raise ValueError(f"{backend} is not a valid backend type. Choose backend from {BackendType.valid_types()}")
+
+        # New OpenAI-compatible REST API (toggleable via enable_rest boolean)
+        if enable_rest:
+            app = FastAPI(title="WhisperLive OpenAI-Compatible API")
+            origins = [o.strip() for o in cors_origins.split(',')] if cors_origins else []
+            app.add_middleware(
+                CORSMiddleware,
+                allow_origins=origins,
+                allow_credentials=True,
+                allow_methods=["*"],  # Allows all methods (GET, POST, etc.)
+                allow_headers=["*"],  # Allows all headers
+            )
+
+
+            @app.post("/v1/audio/transcriptions")
+            async def transcribe(
+                file: UploadFile,
+                model: str = Form(default="whisper-1"),
+                language: Optional[str] = Form(default=None),
+                prompt: Optional[str] = Form(default=None),
+                response_format: str = Form(default="json"),
+                temperature: float = Form(default=0.0),
+                timestamp_granularities: Optional[List[str]] = Form(default=None),
+                # Stubs for unsupported OpenAI params
+                chunking_strategy: Optional[str] = Form(default=None),
+                include: Optional[List[str]] = Form(default=None),
+                known_speaker_names: Optional[List[str]] = Form(default=None),
+                known_speaker_references: Optional[List[str]] = Form(default=None),
+                stream: bool = Form(default=False)
+            ):
+                if stream:
+                    return JSONResponse({"error": "Streaming not supported in this backend."}, status_code=400)
+                if chunking_strategy or known_speaker_names or known_speaker_references:
+                    logging.warning("Diarization/chunking params ignored; not supported.")
+
+                supported_formats = ["json", "text", "srt", "verbose_json", "vtt"]
+                if response_format not in supported_formats:
+                    return JSONResponse({"error": f"Unsupported response_format. Supported: {supported_formats}"}, status_code=400)
+
+                if model != "whisper-1":
+                    logging.warning(f"Model '{model}' requested; using 'small' as fallback.")
+                model_name = faster_whisper_custom_model_path or "small"
+
+                try:
+                    suffix = os.path.splitext(file.filename)[1] or ".wav"
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+                        shutil.copyfileobj(file.file, tmp)
+                        tmp_path = tmp.name
+
+                    device = "cuda" if torch.cuda.is_available() else "cpu"
+                    compute_type = "float16" if device == "cuda" else "int8"
+
+                    transcriber = WhisperModel(model_name, device=device, compute_type=compute_type)
+                    segments, info = transcriber.transcribe(
+                        tmp_path,
+                        language=language,
+                        initial_prompt=prompt,
+                        temperature=temperature,
+                        vad_filter=False,
+                        word_timestamps=(timestamp_granularities and "word" in timestamp_granularities)
+                    )
+
+                    text = " ".join([s.text.strip() for s in segments])
+                    os.unlink(tmp_path)
+
+                    if response_format == "text":
+                        return PlainTextResponse(text)
+                    elif response_format == "json":
+                        return {"text": text}
+                    elif response_format == "verbose_json":
+                        verbose = {
+                            "task": "transcribe",
+                            "language": info.language,
+                            "duration": info.duration,
+                            "text": text,
+                            "segments": []
+                        }
+                        for seg in segments:
+                            seg_dict = {
+                                "id": seg.id,
+                                "seek": seg.seek,
+                                "start": seg.start,
+                                "end": seg.end,
+                                "text": seg.text.strip(),
+                                "tokens": seg.tokens,
+                                "temperature": seg.temperature,
+                                "avg_logprob": seg.avg_logprob,
+                                "compression_ratio": seg.compression_ratio,
+                                "no_speech_prob": seg.no_speech_prob
+                            }
+                            if timestamp_granularities and "word" in timestamp_granularities:
+                                seg_dict["words"] = [{"word": w.word, "start": w.start, "end": w.end, "probability": w.probability} for w in seg.words]
+                            verbose["segments"].append(seg_dict)
+                        return verbose
+                    elif response_format in ["srt", "vtt"]:
+                        output = []
+                        for i, seg in enumerate(segments, 1):
+                            start = f"{int(seg.start // 3600):02}:{int((seg.start % 3600) // 60):02}:{seg.start % 60:06.3f}"
+                            end = f"{int(seg.end // 3600):02}:{int((seg.end % 3600) // 60):02}:{seg.end % 60:06.3f}"
+                            if response_format == "srt":
+                                output.append(f"{i}\n{start.replace('.', ',')} --> {end.replace('.', ',')}\n{seg.text.strip()}\n")
+                            else:  # vtt
+                                output.append(f"{start} --> {end}\n{seg.text.strip()}\n")
+                        return PlainTextResponse("\n".join(output))
+                except Exception as e:
+                    return JSONResponse({"error": str(e)}, status_code=500)
+
+            threading.Thread(
+                target=uvicorn.run,
+                args=(app,),
+                kwargs={"host": "0.0.0.0", "port": rest_port, "log_level": "info"},
+                daemon=True
+            ).start()
+            logging.info(f"✅ OpenAI-Compatible API started on http://0.0.0.0:{rest_port}")
+
+        # Original WebSocket server (always supported)
         with serve(
             functools.partial(
                 self.recv_audio,
@@ -486,5 +614,4 @@ class TranscriptionServer:
             # Wait for translation thread to finish
             if hasattr(client, 'translation_thread') and client.translation_thread:
                 client.translation_thread.join(timeout=2.0)
-            self.client_manager.remove_client(websocket)
-
+            self.client_manager.remove_client(websocket)