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import torch | |
from transformers import pipeline | |
from transformers.pipelines.audio_utils import ffmpeg_read | |
import gradio as gr | |
MODEL_NAME = "seiching/whisper-small-seiching" | |
#MODEL_NAME = "openai/whisper-small" | |
BATCH_SIZE = 8 | |
device = 0 if torch.cuda.is_available() else "cpu" | |
pipe = pipeline( | |
task="automatic-speech-recognition", | |
model=MODEL_NAME, | |
chunk_length_s=30, | |
device=device, | |
) | |
from openai import OpenAI | |
from concurrent.futures import ThreadPoolExecutor | |
import tiktoken | |
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, | |
# ) | |
#openaiojb =OpenAI(base_url="http://localhost:1234/v1", api_key="not-needed") | |
openaiojb =OpenAI( 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 = "."): | |
if seconds is not None: | |
milliseconds = round(seconds * 1000.0) | |
hours = milliseconds // 3_600_000 | |
milliseconds -= hours * 3_600_000 | |
minutes = milliseconds // 60_000 | |
milliseconds -= minutes * 60_000 | |
seconds = milliseconds // 1_000 | |
milliseconds -= seconds * 1_000 | |
hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" | |
return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" | |
else: | |
# we have a malformed timestamp so just return it as is | |
return seconds | |
def transcribe(file, task, return_timestamps): | |
outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task,"language": "chinese",}, return_timestamps=return_timestamps) | |
text = outputs["text"] | |
if return_timestamps: | |
timestamps = outputs["chunks"] | |
timestamps = [ | |
f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}" | |
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 | |
demo = gr.Blocks() | |
mic_transcribe = gr.Interface( | |
fn=transcribe, | |
inputs=[ | |
gr.inputs.Audio(source="microphone", type="filepath", optional=True), | |
gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), | |
gr.inputs.Checkbox(default=False, label="Return timestamps"), | |
], | |
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" | |
), | |
allow_flagging="never", | |
) | |
file_transcribe = gr.Interface( | |
fn=transcribe, | |
inputs=[ | |
gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"), | |
gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), | |
gr.inputs.Checkbox(default=False, label="Return timestamps"), | |
], | |
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", | |
) | |
def writenotes( apikeystr,inputscript): | |
#text=transcribe_text | |
#openaikey.set_key(inputkey) | |
#openaikey = OpenAIKeyClass(inputkey) | |
print('ok') | |
if len(inputscript)>10: | |
transcribe_text=inputscript | |
ainotestext=process_chunks(apikeystr,transcribe_text) | |
# ainotestext=inputscript | |
#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..."),gr.Textbox(label="逐字稿",placeholder="請輸入逐字稿")], | |
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([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) |