修改英文展示

app.py

@@ -158,8 +158,8 @@ def download_audio(input_wav_path):
     else:
         return None
 
-# 创建Gradio界面
-with gr.Blocks(css=custom_css) as demo:
+# 创建 Gradio 界面
+with gr.Blocks() as demo:
     # 添加标题
     gr.Markdown(
         f"""
@@ -176,13 +176,13 @@ with gr.Blocks(css=custom_css) as demo:
         with gr.Column(scale=1):
             audio_input = gr.Audio(label="Record", type="filepath")
         with gr.Column(scale=1, min_width=300):  # 给输出框设置最小宽度，确保等高对齐
-            output_text = gr.Textbox(label=" Output", lines=8, placeholder="The generated result will be displayed here...", interactive=False)
+            output_text = gr.Textbox(label="Output", lines=8, placeholder="The generated result will be displayed here...", interactive=False)
 
     # 添加任务选择和自定义输入框
     with gr.Row():
         task_dropdown = gr.Dropdown(
             label="Task",
-            choices=list(TASK_PROMPT_MAPPING.keys()) + ["Custom Input Text"],  # 新增选项
+            choices=list(TASK_PROMPT_MAPPING.keys()) + ["Custom Task Prompt"],  # 新增选项
             value="ASR (Automatic Speech Recognition)"
         )
         custom_prompt_input = gr.Textbox(label="Custom Task Prompt", placeholder="Please enter a custom task prompt...", visible=False)  # 新增文本输入框
@@ -202,7 +202,7 @@ with gr.Blocks(css=custom_css) as demo:
             container=False,
             elem_classes="confirmation-buttons"
         )
-        save_button = gr.Button("提交反馈", variant="secondary")
+        save_button = gr.Button("Submit Feedback", variant="secondary")
 
     # 添加底部内容
     with gr.Row():
@@ -229,15 +229,16 @@ with gr.Blocks(css=custom_css) as demo:
         return gr.update(visible=False)
 
     def handle_submit(input_wav_path, task_choice, custom_prompt):
-        if task_choice == "自主输入文本":
+        if task_choice == "Custom Task Prompt":
             input_prompt = custom_prompt  # 使用用户输入的自定义文本
         else:
             input_prompt = TASK_PROMPT_MAPPING.get(task_choice, "未知任务类型")  # 使用预定义的提示
         output_res = do_decode(input_wav_path, input_prompt)
         return output_res
 
+    # 当任务选择框的值发生变化时，更新自定义输入框的可见性
     task_dropdown.change(
-        fn=lambda choice: gr.update(visible=choice == "自主输入文本"),
+        fn=lambda choice: gr.update(visible=choice == "Custom Task Prompt"),
         inputs=task_dropdown,
         outputs=custom_prompt_input
     )
@@ -264,5 +265,5 @@ with gr.Blocks(css=custom_css) as demo:
         outputs=confirmation_row
     )
 
-if __name__== "__main__":
+if __name__ == "__main__":
     demo.launch()

app_old.py

@@ -1,64 +1,269 @@
+import base64
+import json
+import time
+from types import SimpleNamespace
+
+# import spaces
+
 import gradio as gr
-from huggingface_hub import InferenceClient
+import os
 
+import sys
+
+import yaml
+
+sys.path.insert(0, './')
+# from wenet.utils.init_tokenizer import init_tokenizer
+# from wenet.utils.init_model import init_model
+import logging
+# import librosa
+# import torch
+# import torchaudio
+import numpy as np
+def makedir_for_file(filepath):
+    dirpath = os.path.dirname(filepath)
+    if not os.path.exists(dirpath):
+        os.makedirs(dirpath)
+def load_dict_from_yaml(file_path: str):
+    with open(file_path, 'rt', encoding='utf-8') as f:
+        dict_1 = yaml.load(f, Loader=yaml.FullLoader)
+    return dict_1
+
+
+# 获取当前脚本文件的绝对路径
+abs_path = os.path.abspath(__file__)
+# 将图片转换为 Base64
+with open(os.path.join(os.path.dirname(abs_path), "lab.png"), "rb") as image_file:
+    encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
+
+# with open("./cat.jpg", "rb") as image_file:
+#     encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
+
+# 自定义CSS样式
+custom_css = """
+/* 自定义CSS样式 */
 """
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# 任务提示映射
+TASK_PROMPT_MAPPING = {
+    "ASR (Automatic Speech Recognition)": "执行语音识别任务，将音频转换为文字。",
+    "SRWT (Speech Recognition with Timestamps)": "请转录音频内容，并为每个英文词汇及其对应的中文翻译标注出精确到0.1秒的起止时间，时间范围用<>括起来。",
+    "VED (Vocal Event Detection)(Categories:laugh，cough，cry，screaming，sigh，throat clearing，sneeze，other)": "请将音频转录为文字记录，并在记录末尾标注<音频事件>标签，音频事件共8种：laugh，cough，cry，screaming，sigh，throat clearing，sneeze，other。",
+    "SER (Speech Emotion Recognition)(Categories:sad，anger，neutral，happy，surprise，fear，disgust，和other)": "请将音频内容转录成文字记录，并在记录末尾标注<情感>标签，情感共8种：sad，anger，neutral，happy，surprise，fear，disgust，和other。",
+    "SSR (Speaking Style Recognition)(Categories:新闻科普，恐怖故事，童话故事，客服，诗歌散文，有声书，日常口语，其他)": "请将音频内容进行文字转录，并在最后添加<风格>标签，标签共8种：新闻科普、恐怖故事、童话故事、客服、诗歌散文、有声书、日常口语、其他。",
+    "SGC (Speaker Gender Classification)(Categories:female,male)": "请将音频转录为文本，并在文本结尾处标注<性别>标签，性别为female或male。",
+    "SAP (Speaker Age Prediction)(Categories:child、adult和old)": "请将音频转录为文本，并在文本结尾处标注<年龄>标签，年龄划分为child、adult和old三种。",
+    "STTC (Speech to Text Chat)": "首先将语音转录为文字，然后对语音内容进行回复，转录和文字之间使用<开始回答>分割。"
+}
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def init_model_my():
+    logging.basicConfig(level=logging.DEBUG,
+                        format='%(asctime)s %(levelname)s %(message)s')
+    config_path = "train.yaml"
+    from huggingface_hub import hf_hub_download
+    # 从Hugging Face下载.pt文件
+    pt_file_path = hf_hub_download(repo_id="ASLP-lab/OSUM", filename="infer.pt")
+    args = SimpleNamespace(**{
+        "checkpoint": pt_file_path,
+    })
+    configs = load_dict_from_yaml(config_path)
+    model, configs = init_model(args, configs)
+    model = model.cuda()
+    tokenizer = init_tokenizer(configs)
+    print(model)
+    return model, tokenizer
 
-    messages.append({"role": "user", "content": message})
+# global_model, tokenizer = init_model_my()
+print("model init success")
+def do_resample(input_wav_path, output_wav_path):
+    """"""
+    print(f'input_wav_path: {input_wav_path}, output_wav_path: {output_wav_path}')
+    waveform, sample_rate = torchaudio.load(input_wav_path)
+    # 检查音频的维度
+    num_channels = waveform.shape[0]
+    # 如果音频是多通道的，则进行通道平均
+    if num_channels > 1:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+    waveform = torchaudio.transforms.Resample(
+        orig_freq=sample_rate, new_freq=16000)(waveform)
+    makedir_for_file(output_wav_path)
+    torchaudio.save(output_wav_path, waveform, 16000)
 
-    response = ""
+# @spaces.GPU
+def true_decode_fuc(input_wav_path, input_prompt):
+    # input_prompt = TASK_PROMPT_MAPPING.get(input_prompt, "未知任务类型")
+    print(f"wav_path: {input_wav_path}, prompt:{input_prompt}")
+    timestamp_ms = int(time.time() * 1000)
+    now_file_tmp_path_resample = f'/home/xlgeng/.cache/.temp/{timestamp_ms}_resample.wav'
+    do_resample(input_wav_path, now_file_tmp_path_resample)
+    input_wav_path = now_file_tmp_path_resample
+    waveform, sample_rate = torchaudio.load(input_wav_path)
+    waveform = waveform.squeeze(0)  # (channel=1, sample) -> (sample,)
+    print(f'wavform shape: {waveform.shape}, sample_rate: {sample_rate}')
+    window = torch.hann_window(400)
+    stft = torch.stft(waveform,
+                      400,
+                      160,
+                      window=window,
+                      return_complex=True)
+    magnitudes = stft[..., :-1].abs() ** 2
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    filters = torch.from_numpy(
+        librosa.filters.mel(sr=sample_rate,
+                            n_fft=400,
+                            n_mels=80))
+    mel_spec = filters @ magnitudes
 
-        response += token
-        yield response
+    # NOTE(xcsong): https://github.com/openai/whisper/discussions/269
+    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+    log_spec = (log_spec + 4.0) / 4.0
+    feat = log_spec.transpose(0, 1)
+    feat_lens = torch.tensor([feat.shape[0]], dtype=torch.int64).cuda()
+    feat = feat.unsqueeze(0).cuda()
+    # feat = feat.half()
+    # feat_lens = feat_lens.half()
+    model = global_model.cuda()
+    model.eval()
+    res_text = model.generate(wavs=feat, wavs_len=feat_lens, prompt=input_prompt)[0]
+    print("耿雪龙哈哈：", res_text)
+    return res_text
 
+def do_decode(input_wav_path, input_prompt):
+    print(f'input_wav_path= {input_wav_path}, input_prompt= {input_prompt}')
+    # 省略处理逻辑
+    # output_res= true_decode_fuc(input_wav_path, input_prompt)
+    output_res = f"耿雪龙哈哈：测试结果, input_wav_path= {input_wav_path}, input_prompt= {input_prompt}"
+    return output_res
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def save_to_jsonl(if_correct, wav, prompt, res):
+    data = {
+        "if_correct": if_correct,
+        "wav": wav,
+        "task": prompt,
+        "res": res
+    }
+    with open("results.jsonl", "a", encoding="utf-8") as f:
+        f.write(json.dumps(data, ensure_ascii=False) + "\n")
+
+def handle_submit(input_wav_path, input_prompt):
+    output_res = do_decode(input_wav_path, input_prompt)
+    return output_res
+
+def download_audio(input_wav_path):
+    if input_wav_path:
+        # 返回文件路径供下载
+        return input_wav_path
+    else:
+        return None
+
+# 创建 Gradio 界面
+with gr.Blocks() as demo:
+    # 添加标题
+    gr.Markdown(
+        f"""
+        <div style="display: flex; align-items: center; justify-content: center; text-align: center;">
+            <h1 style="font-family: 'Arial', sans-serif; color: #014377; font-size: 32px; margin-bottom: 0; display: inline-block; vertical-align: middle;">
+                OSUM Speech Understanding Model Test
+            </h1>
+        </div>
+        """
+    )
+
+    # 添加音频输入和任务选择
+    with gr.Row():
+        with gr.Column(scale=1):
+            audio_input = gr.Audio(label="Record", type="filepath")
+        with gr.Column(scale=1, min_width=300):  # 给输出框设置最小宽度，确保等高对齐
+            output_text = gr.Textbox(label="Output", lines=8, placeholder="The generated result will be displayed here...", interactive=False)
+
+    # 添加任务选择和自定义输入框
+    with gr.Row():
+        task_dropdown = gr.Dropdown(
+            label="Task",
+            choices=list(TASK_PROMPT_MAPPING.keys()) + ["Custom Task Prompt"],  # 新增选项
+            value="ASR (Automatic Speech Recognition)"
+        )
+        custom_prompt_input = gr.Textbox(label="Custom Task Prompt", placeholder="Please enter a custom task prompt...", visible=False)  # 新增文本输入框
+
+    # 添加按钮（下载按钮在左边，开始处理按钮在右边）
+    with gr.Row():
+        download_button = gr.DownloadButton("Download Recording", variant="secondary", elem_classes=["button-height", "download-button"])
+        submit_button = gr.Button("Start to Process", variant="primary", elem_classes=["button-height", "submit-button"])
+
+    # 添加确认组件
+    with gr.Row(visible=False) as confirmation_row:
+        gr.Markdown("Please determine whether the result is correct:")
+        confirmation_buttons = gr.Radio(
+            choices=["Correct", "Incorrect"],
+            label="",
+            interactive=True,
+            container=False,
+            elem_classes="confirmation-buttons"
+        )
+        save_button = gr.Button("Submit Feedback", variant="secondary")
+
+    # 添加底部内容
+    with gr.Row():
+        # 底部内容容器
+        with gr.Column(scale=1, min_width=800):  # 设置最小宽度以确保内容居中
+            gr.Markdown(
+                f"""
+                <div style="position: fixed; bottom: 20px; left: 50%; transform: translateX(-50%); display: flex; align-items: center; justify-content: center; gap: 20px;">
+                    <div style="text-align: center;">
+                        <p style="margin: 0;"><strong>Audio, Speech and Language Processing Group (ASLP@NPU),</strong></p>
+                        <p style="margin: 0;"><strong>Northwestern Polytechnical University</strong></p>
+                    </div>
+                    <img src="data:image/png;base64,{encoded_string}" alt="OSUM Logo" style="height: 80px; width: auto;">
+                </div>
+                """
+            )
+
+    # 绑定事件
+    def show_confirmation(output_res, input_wav_path, input_prompt):
+        return gr.update(visible=True), output_res, input_wav_path, input_prompt
+
+    def save_result(if_correct, wav, prompt, res):
+        save_to_jsonl(if_correct, wav, prompt, res)
+        return gr.update(visible=False)
+
+    def handle_submit(input_wav_path, task_choice, custom_prompt):
+        if task_choice == "Custom Task Prompt":
+            input_prompt = custom_prompt  # 使用用户输入的自定义文本
+        else:
+            input_prompt = TASK_PROMPT_MAPPING.get(task_choice, "未知任务类型")  # 使用预定义的提示
+        output_res = do_decode(input_wav_path, input_prompt)
+        return output_res
+
+    # 当任务选择框的值发生变化时，更新自定义输入框的可见性
+    task_dropdown.change(
+        fn=lambda choice: gr.update(visible=choice == "Custom Task Prompt"),
+        inputs=task_dropdown,
+        outputs=custom_prompt_input
+    )
+
+    submit_button.click(
+        fn=handle_submit,
+        inputs=[audio_input, task_dropdown, custom_prompt_input],
+        outputs=output_text
+    ).then(
+        fn=show_confirmation,
+        inputs=[output_text, audio_input, task_dropdown],
+        outputs=[confirmation_row, output_text, audio_input, task_dropdown]
+    )
+
+    download_button.click(
+        fn=download_audio,
+        inputs=[audio_input],
+        outputs=[download_button]  # 输出到 download_button
+    )
 
+    save_button.click(
+        fn=save_result,
+        inputs=[confirmation_buttons, audio_input, task_dropdown, output_text],
+        outputs=confirmation_row
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()