add tiktoken package及openai

app.py

@@ -16,6 +16,96 @@ pipe = pipeline(
     device=device,
 )
 
+from openai import OpenAI
+from concurrent.futures import ThreadPoolExecutor
+import tiktoken
+#from google.colab import userdata
+# class OpenAIKeyClass:
+#     def __init__(self, api_key):
+#         self.api_key = api_key
+
+#     def get_key(self):
+
+#         return self.api_key
+
+#     def set_key(self, api_key):
+#        self.api_key = api_key
+
+# # 建立一個 OpenAIKeyClass 物件
+# openaikey=OpenAIKeyClass("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")
+# # Add your own OpenAI API key
+# client = OpenAI(
+#     # This is the default and can be omitted
+
+#     api_key=openaikey.get_key(),
+# )
+
+
+
+def call_openai_api(openaiobj,transcription):
+
+    response = openaiobj.chat.completions.create(
+        model="gpt-3.5-turbo",
+        temperature=0,
+        messages=[
+            {
+                "role": "system",
+                "content": "你是專業的會議紀錄製作員,請根據由語音辨識軟體將會議錄音所轉錄的逐字稿，也請注意逐字稿可能有錯,請以條列式,列出討論事項及結論，討論內容細節請略過，要用比較正式及容易閱讀的寫法，避免口語化"
+            },
+            {
+                "role": "user",
+                "content": transcription
+            }
+        ]
+    )
+    return response.choices[0].message.content
+
+
+def split_into_chunks(text, tokens=500):
+    encoding = tiktoken.encoding_for_model('gpt-3.5-turbo')
+    words = encoding.encode(text)
+    chunks = []
+    for i in range(0, len(words), tokens):
+        chunks.append(' '.join(encoding.decode(words[i:i + tokens])))
+    return chunks
+
+def process_chunks(openaikeystr,inputtext):
+    openaiobj = OpenAI(
+    # This is the default and can be omitted
+
+    api_key=openaikeystr,
+    )
+
+    text = inputtext
+    #openaikey.set_key(openaikeystr)
+    #print('process_chunk',openaikey.get_key())
+    chunks = split_into_chunks(text)
+    response=''
+    for chunk in chunks:
+       response=response+call_openai_api(openaiobj,chunk)
+    return response
+    # # Processes chunks in parallel
+    # with ThreadPoolExecutor() as executor:
+    #     responses = list(executor.map(call_openai_api, [openaiobj,chunks]))
+    # return responses
+import torch
+from transformers import pipeline
+from transformers.pipelines.audio_utils import ffmpeg_read
+import gradio as gr
+
+MODEL_NAME = "seiching/whisper-small-seiching"
+BATCH_SIZE = 8
+transcribe_text="this is a test"
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device,
+)
+
 
 # Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
 def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
@@ -48,6 +138,15 @@ def transcribe(file, task, return_timestamps):
             for chunk in timestamps
         ]
         text = "\n".join(str(feature) for feature in timestamps)
+    global transcribe_text
+    transcribe_text=text
+    # with open('asr_resul.txt', 'w') as f:
+    #   f.write(text)
+
+    # ainotes=process_chunks(text)
+    # with open("ainotes_result.txt", "a") as f:
+    #   f.write(ainotes)
+
     return text
 
 
@@ -63,15 +162,14 @@ mic_transcribe = gr.Interface(
     outputs="text",
     layout="horizontal",
     theme="huggingface",
-    title="Whisper Demo: Transcribe Audio",
+    title="會議紀錄小幫手AINotes",
     description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
+        "可由麥克風錄音或上傳語音檔"
+        f" 使用這個模型 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 先做語音辨識再做會議紀錄摘要"
+        " 長度沒有限制"
     ),
     allow_flagging="never",
 )
-
 file_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
@@ -82,21 +180,54 @@ file_transcribe = gr.Interface(
     outputs="text",
     layout="horizontal",
     theme="huggingface",
-    title="Whisper Demo: Transcribe Audio",
+    title="會議紀錄小幫手AINotes",
     description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
+        "可由麥克風錄音或上傳語音檔"
+        f" 使用這個模型 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 先做語音辨識再做會議紀錄摘要"
+        " 長度沒有限制"
     ),
-    examples=[
-        ["./example.flac", "transcribe", False],
-        ["./example.flac", "transcribe", True],
-    ],
+    # examples=[
+    #     ["./example.flac", "transcribe", False],
+    #     ["./example.flac", "transcribe", True],
+    # ],
+    cache_examples=True,
+    allow_flagging="never",
+)
+def writenotes(apikeystr):
+  #text=transcribe_text
+  #openaikey.set_key(inputkey)
+  #openaikey = OpenAIKeyClass(inputkey)
+  print('ok')
+  ainotestext=process_chunks(apikeystr,transcribe_text)
+  #ainotestext=""
+  # with open('asr_resul.txt', 'w') as f:
+  #     #print(transcribe_text)
+  #    # f.write(inputkey)
+  #     f.write(transcribe_text)
+  # with open('ainotes.txt','w') as f:
+  #   f.write(ainotestext)
+  return ainotestext
+ainotes = gr.Interface(
+    fn=writenotes,
+    inputs=gr.Textbox(label="OPEN AI API KEY",placeholder="請輸入sk..."),
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="會議紀錄小幫手AINotes",
+    description=(
+        "可由麥克風錄音或上傳語音檔"
+        f" 使用這個模型 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 先做語音辨識再做會議紀錄摘要"
+        " 長度沒有限制"
+    ),
+    # examples=[
+    #     ["./example.flac", "transcribe", False],
+    #     ["./example.flac", "transcribe", True],
+    # ],
     cache_examples=True,
     allow_flagging="never",
 )
 
 with demo:
-    gr.TabbedInterface([mic_transcribe, file_transcribe], ["Transcribe Microphone", "Transcribe Audio File"])
+    gr.TabbedInterface([file_transcribe,mic_transcribe,ainotes], ["語音檔辨識","麥克風語音檔辨識","產生會議紀錄" ])
 
 demo.launch(enable_queue=True)

requirements.txt

@@ -1,3 +1,4 @@
 --extra-index-url https://download.pytorch.org/whl/cu113
 torch
 transformers
+tiktoken