Create app.py

app.py

@@ -0,0 +1,96 @@
+import json
+import requests
+
+from datetime import datetime
+
+import time
+import traceback
+
+API_URL = "https://api-inference.huggingface.co/models/"
+
+
+def date_now():
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+def record_opt(msg):
+    return f"{date_now()} {msg}\n"
+
+
+def speech_recognize(audio, model_name, hf_token, opt):
+    opt += record_opt("转录开始 ...")
+    yield "转录中，请稍等...", opt
+    start = time.monotonic()
+
+    with open(audio, "rb") as f:
+        data = f.read()
+    try:
+        url = API_URL + model_name
+        print(f">>> url is {url}")
+        headers = {"Authorization": f"Bearer {hf_token}"}
+        response = requests.request("POST", url, headers=headers, data=data)
+        text = json.loads(response.content.decode("utf-8"))
+        print(f">>> text is {text}")
+        text = text['text']
+    except:
+        text = f"转录失败：\n{traceback.format_exc()}"
+
+    cost = time.monotonic() - start
+    opt += record_opt(f"转录结束，耗时{cost:.3f}s")
+    yield text, opt
+
+import gradio as gr
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h2 align="center">Automatic Speech Recognition (OpenAI Whisper with Inference API)</h2>""")
+    with gr.Row():
+        gr.Markdown(
+            """🤗 调用 huggingface API，使用 OpenAI Whisper 模型进行语音识别，也可以成为语音转文本（Speech to Text, STT）
+
+            👉 目的是练习使用 Gradio Audio 组件和探索使用 Huggingface Inference API
+            """
+        )
+    with gr.Row():
+        with gr.Column():
+            audio = gr.Audio(source="microphone", type="filepath")
+            model_name = gr.Dropdown(
+                label="选择模型",
+                choices=[
+                    "openai/whisper-large-v2",
+                    "openai/whisper-large",
+                    "openai/whisper-medium",
+                    "openai/whisper-small",
+                    "openai/whisper-base",
+                    "openai/whisper-tiny",
+                ],
+                value="openai/whisper-large-v2",
+            )
+            hf_token = gr.Textbox(label="Huggingface token")
+        with gr.Column():
+            output = gr.Textbox(label="转录结果")
+            operation = gr.Textbox(label="组件操作历史")
+    audio.start_recording(
+        lambda x: x + record_opt("开始录音 ..."),
+        inputs=operation, outputs=operation
+    )
+    audio.play(
+        lambda x: x + record_opt("播放录音"),
+        inputs=operation, outputs=operation
+    )
+    audio.pause(
+        lambda x: x + record_opt("暂停播放"),
+        inputs=operation, outputs=operation
+    )
+    audio.stop(
+        lambda x: x + record_opt("停止播放"),
+        inputs=operation, outputs=operation
+    )
+    audio.end(
+        lambda x: x + record_opt("播放完毕"),
+        inputs=operation, outputs=operation
+    )
+    audio.stop_recording(speech_recognize, inputs=[audio, model_name, hf_token, operation], outputs=[output, operation])
+
+demo.queue(max_size=128, concurrency_count=16)
+demo.launch(debug=True)
+
+