fix gpt refine prompt

.dockerignore

@@ -1 +1,2 @@
 /venv
+/.git

.gitignore

@@ -1 +1,2 @@
 /venv
+/.git

README.md

@@ -10,6 +10,21 @@ Whisper-FastAPI is a very simple Python FastAPI interface for konele and OpenAI
 - **Audio Transcriptions**: The `/v1/audio/transcriptions` endpoint allows users to upload an audio file and receive transcription in response, with an optional `response_type` parameter. The `response_type` can be 'json', 'text', 'tsv', 'srt', and 'vtt'.
 - **Simplified Chinese**: The traditional Chinese will be automatically convert to simplified Chinese for konele using `opencc` library.
 
+## GPT Refine Result
+
+You can choose to use the OpenAI GPT model for post-processing transcription results. You can also provide context to GPT to allow it to modify the text based on your context.
+
+Set the environment variables `OPENAI_BASE_URL=https://api.openai.com/v1` and `OPENAI_API_KEY=your-sk` to enable this feature.
+
+When the client sends a request with `gpt_refine=True`, this feature will be activated. Specifically:
+
+- For `/v1/audio/transcriptions`, submit using `curl <api_url> -F file=audio.mp4 -F gpt_refine=True`.
+- For `/v1/konele/ws` and `/v1/konele/post`, use the URL format `/v1/konele/ws/gpt_refine`.
+
+The default model is `gpt-4o-mini` set by environment variable `OPENAI_LLM_MODEL`.
+
+You can easily edit the code LLM's prompt to better fit your workflow. It's just a few lines of code. Give it a try, it's very simple!
+
 ## Usage
 
 ### Konele Voice Typing
@@ -19,7 +34,7 @@ For konele voice typing, you can use either the websocket endpoint or the POST m
 - **Websocket**: Connect to the websocket at `/konele/ws` (or `/v1/konele/ws`) and send audio data. The server will respond with the transcription or translation.
 - **POST Method**: Send a POST request to `/konele/post` (or `/v1/konele/post`) with the audio data in the body. The server will respond with the transcription or translation.
 
-You can also use the demo I have created to quickly test the effect at <https://yongyuancv.cn/v1/konele/ws> and <https://yongyuancv.cn/v1/konele/post>
+You can also use the demo I have created to quickly test the effect at <https://yongyuancv.cn/v1/konele/post>
 
 ### OpenAI Whisper Service
 

requirements.txt

@@ -6,3 +6,4 @@ opencc
 prometheus-fastapi-instrumentator
 git+https://github.com/heimoshuiyu/faster-whisper@a759f5f48f5ef5b79461a6461966eafe9df088a9
 pydub
+aiohttp

requirements_version.txt

@@ -1,6 +1,11 @@
+aiohappyeyeballs==2.4.4
+aiohttp==3.11.10
+aiosignal==1.3.1
 annotated-types==0.7.0
-anyio==4.6.2.post1
-av==13.1.0
+anyio==4.7.0
+async-timeout==5.0.1
+attrs==24.2.0
+av==14.0.0
 certifi==2024.8.30
 cffi==1.17.1
 charset-normalizer==3.4.0
@@ -8,42 +13,46 @@ click==8.1.7
 coloredlogs==15.0.1
 ctranslate2==4.5.0
 exceptiongroup==1.2.2
-fastapi==0.115.5
+fastapi==0.115.6
 faster-whisper @ git+https://github.com/heimoshuiyu/faster-whisper@a759f5f48f5ef5b79461a6461966eafe9df088a9
 filelock==3.16.1
 flatbuffers==24.3.25
+frozenlist==1.5.0
 fsspec==2024.10.0
 h11==0.14.0
 httptools==0.6.4
-huggingface-hub==0.26.2
+huggingface-hub==0.26.3
 humanfriendly==10.0
 idna==3.10
 mpmath==1.3.0
+multidict==6.1.0
 numpy==2.1.3
 onnxruntime==1.20.1
 OpenCC==1.1.9
 packaging==24.2
 prometheus-fastapi-instrumentator==7.0.0
-prometheus_client==0.21.0
-protobuf==5.28.3
+prometheus_client==0.21.1
+propcache==0.2.1
+protobuf==5.29.1
 pycparser==2.22
-pydantic==2.10.1
+pydantic==2.10.3
 pydantic_core==2.27.1
 pydub==0.25.1
 python-dotenv==1.0.1
-python-multipart==0.0.17
+python-multipart==0.0.19
 PyYAML==6.0.2
 requests==2.32.3
 sniffio==1.3.1
 sounddevice==0.5.1
 starlette==0.41.3
 sympy==1.13.3
-tokenizers==0.20.3
-tqdm==4.67.0
+tokenizers==0.21.0
+tqdm==4.67.1
 typing_extensions==4.12.2
 urllib3==2.2.3
 uvicorn==0.32.1
 uvloop==0.21.0
-watchfiles==0.24.0
+watchfiles==1.0.0
 websockets==14.1
-whisper-ctranslate2==0.4.8
+whisper-ctranslate2==0.5.0
+yarl==1.18.3

whisper_fastapi.py

@@ -1,8 +1,10 @@
+import aiohttp
+import os
 import sys
 import dataclasses
 import faster_whisper
 import json
-from fastapi.responses import StreamingResponse
+from fastapi.responses import PlainTextResponse, StreamingResponse
 import wave
 import pydub
 import io
@@ -28,9 +30,12 @@ from prometheus_fastapi_instrumentator import Instrumentator
 
 # redirect print to stderr
 _print = print
+
+
 def print(*args, **kwargs):
     _print(*args, file=sys.stderr, **kwargs)
 
+
 parser = argparse.ArgumentParser()
 parser.add_argument("--host", default="0.0.0.0", type=str)
 parser.add_argument("--port", default=5000, type=int)
@@ -65,6 +70,51 @@ app.add_middleware(
 )
 
 
+async def gpt_refine_text(
+    ge: Generator[Segment, None, None], info: TranscriptionInfo, context: str
+) -> str:
+    text = build_json_result(ge, info).text.strip()
+    model = os.environ.get("OPENAI_LLM_MODEL", "gpt-4o-mini")
+    if not text:
+        return ""
+    body: dict = {
+        "model": model,
+        "temperature": 0.1,
+        "stream": False,
+        "messages": [
+            {
+                "role": "system",
+                "content": f"""
+You are a audio transcription text refiner. You may refer to the context to correct the transcription text. 
+Your task is to correct the transcribed text by removing redundant and repetitive words, resolving any contradictions, and fixing punctuation errors.
+Keep my spoken language as it is, and do not change my speaking style. Only fix the text.
+Response directly with the text.
+                    """.strip(),
+            },
+            {
+                "role": "user",
+                "content": f"""
+context: {context}
+---
+transcription: {text}
+                     """.strip(),
+            },
+        ],
+    }
+    print(f"Refining text length: {len(text)} with {model}")
+    print(body)
+    async with aiohttp.ClientSession() as session:
+        async with session.post(
+            os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1")
+            + "/chat/completions",
+            json=body,
+            headers={
+                "Authorization": f'Bearer {os.environ["OPENAI_API_KEY"]}',
+            },
+        ) as response:
+            return (await response.json())["choices"][0]["message"]["content"]
+
+
 def stream_writer(generator: Generator[Segment, Any, None]):
     for segment in generator:
         yield "data: " + json.dumps(segment, ensure_ascii=False) + "\n\n"
@@ -169,8 +219,12 @@ async def konele_status(
 
 @app.websocket("/k6nele/ws")
 @app.websocket("/konele/ws")
+@app.websocket("/konele/ws/gpt_refine")
+@app.websocket("/k6nele/ws/gpt_refine")
 @app.websocket("/v1/k6nele/ws")
 @app.websocket("/v1/konele/ws")
+@app.websocket("/v1/konele/ws/gpt_refine")
+@app.websocket("/v1/k6nele/ws/gpt_refine")
 async def konele_ws(
     websocket: WebSocket,
     task: Literal["transcribe", "translate"] = "transcribe",
@@ -218,13 +272,17 @@ async def konele_ws(
         language=None if lang == "und" else lang,
         initial_prompt=initial_prompt,
     )
-    result = build_json_result(generator, info)
+
+    if websocket.url.path.endswith("gpt_refine"):
+        result = await gpt_refine_text(generator, info, initial_prompt)
+    else:
+        result = build_json_result(generator, info).text
 
     await websocket.send_json(
         {
             "status": 0,
             "segment": 0,
-            "result": {"hypotheses": [{"transcript": result.text}], "final": True},
+            "result": {"hypotheses": [{"transcript": result}], "final": True},
             "id": md5,
         }
     )
@@ -233,8 +291,12 @@ async def konele_ws(
 
 @app.post("/k6nele/post")
 @app.post("/konele/post")
+@app.post("/k6nele/post/gpt_refine")
+@app.post("/konele/post/gpt_refine")
 @app.post("/v1/k6nele/post")
 @app.post("/v1/konele/post")
+@app.post("/v1/k6nele/post/gpt_refine")
+@app.post("/v1/konele/post/gpt_refine")
 async def translateapi(
     request: Request,
     task: Literal["transcribe", "translate"] = "transcribe",
@@ -279,11 +341,15 @@ async def translateapi(
         language=None if lang == "und" else lang,
         initial_prompt=initial_prompt,
     )
-    result = build_json_result(generator, info)
+
+    if request.url.path.endswith("gpt_refine"):
+        result = await gpt_refine_text(generator, info, initial_prompt)
+    else:
+        result = build_json_result(generator, info).text
 
     return {
         "status": 0,
-        "hypotheses": [{"utterance": result.text}],
+        "hypotheses": [{"utterance": result}],
         "id": md5,
     }
 
@@ -299,6 +365,7 @@ async def transcription(
     language: str = Form("und"),
     vad_filter: bool = Form(False),
     repetition_penalty: float = Form(1.0),
+    gpt_refine: bool = Form(False),
 ):
     """Transcription endpoint
 
@@ -332,6 +399,8 @@ async def transcription(
     elif response_format == "json":
         return build_json_result(generator, info)
     elif response_format == "text":
+        if gpt_refine:
+            return PlainTextResponse(await gpt_refine_text(generator, info, prompt))
         return StreamingResponse(text_writer(generator), media_type="text/plain")
     elif response_format == "tsv":
         return StreamingResponse(tsv_writer(generator), media_type="text/plain")