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

@@ -4,5 +4,6 @@ uvicorn[standard]
 whisper_ctranslate2
 opencc
 prometheus-fastapi-instrumentator
-git+https://github.com/SYSTRAN/faster-whisper@3e0ba86571b9fe93bab2a25b3ff5af1be41014ec
+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
+anyio==4.7.0
-av==13.1.0
+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/SYSTRAN/faster-whisper@3e0ba86571b9fe93bab2a25b3ff5af1be41014ec
+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==1.20.0
+onnxruntime==1.20.1
 OpenCC==1.1.9
 packaging==24.2
 prometheus-fastapi-instrumentator==7.0.0
-prometheus_client==0.21.0
+prometheus_client==0.21.1
-protobuf==5.28.3
+propcache==0.2.1
+protobuf==5.29.1
 pycparser==2.22
-pydantic==2.9.2
+pydantic==2.10.3
-pydantic_core==2.23.4
+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==0.41.2
+starlette==0.41.3
 sympy==1.13.3
-tokenizers==0.20.3
+tokenizers==0.21.0
-tqdm==4.67.0
+tqdm==4.67.1
 typing_extensions==4.12.2
 urllib3==2.2.3
-uvicorn==0.32.0
+uvicorn==0.32.1
 uvloop==0.21.0
-watchfiles==0.24.0
+watchfiles==1.0.0
 websockets==14.1
-whisper-ctranslate2==0.4.7
+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 tqdm
 import json
-from fastapi.responses import StreamingResponse
+from fastapi.responses import PlainTextResponse, StreamingResponse
 import wave
 import pydub
 import io
@@ -21,12 +23,19 @@ from fastapi import (
     WebSocket,
 )
 from fastapi.middleware.cors import CORSMiddleware
-from src.whisper_ctranslate2.whisper_ctranslate2 import Transcribe
 from src.whisper_ctranslate2.writers import format_timestamp
 from faster_whisper.transcribe import Segment, TranscriptionInfo
 import opencc
 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)
@@ -61,6 +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"
@@ -95,7 +149,7 @@ def srt_writer(generator: Generator[Segment, Any, None]):
 
 def vtt_writer(generator: Generator[Segment, Any, None]):
     yield "WEBVTT\n\n"
-    for i, segment in enumerate(generator):
+    for _, segment in enumerate(generator):
         start_time = format_timestamp(segment.start)
         end_time = format_timestamp(segment.end)
         text = segment.text.strip()
@@ -107,15 +161,16 @@ class JsonResult(TranscriptionInfo):
     segments: list[Segment]
     text: str
 
+
 def build_json_result(
     generator: Iterable[Segment],
-    info:  TranscriptionInfo,
+    info: TranscriptionInfo,
 ) -> JsonResult:
     segments = [i for i in generator]
     return JsonResult(
         text="\n".join(i.text for i in segments),
         segments=segments,
-        **dataclasses.asdict(info)
+        **dataclasses.asdict(info),
     )
 
 
@@ -140,14 +195,12 @@ def stream_builder(
         "Detected language '%s' with probability %f"
         % (info.language, info.language_probability)
     )
+
     def wrap():
-        last_pos = 0
+        for segment in segments:
-        with tqdm.tqdm(total=info.duration, unit="seconds", disable=True) as pbar:
+            if info.language == "zh":
-            for segment in segments:
+                segment.text = ccc.convert(segment.text)
-                start, end, text = segment.start, segment.end, segment.text
+            yield segment
-                pbar.update(end - last_pos)
-                last_pos = end
-                yield segment
 
     return wrap(), info
 
@@ -166,8 +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",
@@ -215,13 +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,
         }
     )
@@ -230,8 +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",
@@ -276,11 +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,
     }
 
@@ -296,6 +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
 
@@ -303,9 +373,9 @@ async def transcription(
     """
 
     if not task:
-        if request.url.path == '/v1/audio/transcriptions':
+        if request.url.path == "/v1/audio/transcriptions":
             task = "transcribe"
-        elif request.url.path == '/v1/audio/translations':
+        elif request.url.path == "/v1/audio/translations":
             task = "translate"
         else:
             raise HTTPException(400, "task parameter is required")
@@ -329,6 +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")