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| import gradio as gr | |
| import os | |
| import tempfile | |
| import subprocess | |
| from transformers import pipeline | |
| import torch | |
| from zipfile import ZipFile | |
| from fastapi import FastAPI | |
| app = FastAPI() | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| if torch.cuda.is_available(): | |
| model_id = "openai/whisper-small.en" | |
| else: | |
| model_id = "openai/whisper-tiny.en" | |
| pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model=model_id, | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| def support_gbk(zip_file: ZipFile): | |
| name_to_info = zip_file.NameToInfo | |
| # copy map first | |
| for name, info in name_to_info.copy().items(): | |
| real_name = name.encode("cp437").decode("gbk") | |
| if real_name != name: | |
| info.filename = real_name | |
| del name_to_info[name] | |
| name_to_info[real_name] = info | |
| return zip_file | |
| def handel(f): | |
| if not f: | |
| raise gr.Error("请上传文件") | |
| if f.name.endswith(".zip"): | |
| with support_gbk(ZipFile(f.name, "r")) as z: | |
| dir = tempfile.TemporaryDirectory() | |
| z.extractall(path=dir.name) | |
| return handel_files( | |
| [ | |
| os.path.join(filepath, filename) | |
| for filepath, _, filenames in os.walk(dir.name) | |
| for filename in filenames | |
| ] | |
| ) | |
| else: | |
| return handel_files([f.name]) | |
| def ffmpeg_convert(file_input, file_output): | |
| if subprocess.run(["ffmpeg", "-y", "-i", file_input, file_output]).returncode: | |
| raise gr.Error("ffmpeg_convert 失败, 请检查文件格式是否正确") | |
| def handel_files(f_ls): | |
| files = [] | |
| for file in f_ls: | |
| file_output=None | |
| if file.endswith(".m4a"): | |
| file_output = file.replace(".m4a", ".wav") | |
| ffmpeg_convert(file, file_output) | |
| elif file.endswith(".mp3"): | |
| file_output = file.replace(".mp3", ".wav") | |
| ffmpeg_convert(file, file_output) | |
| elif file.endswith(".wav"): | |
| # check wav file is valid or not | |
| file_output = file+".wav" | |
| ffmpeg_convert(file, file_output) | |
| if file_output: | |
| files.append(file_output) | |
| else: | |
| gr.Warning(f"存在不合法文件{os.path.basename(file)},已跳过处理") | |
| ret = [] | |
| for file in files: | |
| ret.append(whisper_handler(file)) | |
| return "\n\n".join(ret) | |
| def whisper_handler(file): | |
| file_name = os.path.basename(file) | |
| gr.Info(f"处理文件 - {file_name.split('.')[0]}") | |
| return pipe(file)["text"] | |
| with gr.Blocks() as blocks: | |
| f = gr.File(file_types=[".zip", ".mp3", ".wav", ".m4a"]) | |
| b = gr.Button(value="提交") | |
| t = gr.Textbox(label="结果") | |
| b.click(handel, inputs=f, outputs=t) | |
| blocks.queue(max_size=3) | |
| app = gr.mount_gradio_app(app, blocks, path="/") | |