fix: upgrade gradio to 5.23.0 to fix TypeError in api_info

README.md

@@ -4,7 +4,7 @@ emoji: 🚀
 colorFrom: blue
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.9.1
+sdk_version: 5.23.0
 app_file: app.py
 pinned: true
 license: apache-2.0

app_cpu.py

@@ -0,0 +1,124 @@
+import os
+from huggingface_hub import snapshot_download
+
+MODEL_CACHE_DIR = "./models"
+SENSE_VOICE_LOCAL_PATH = os.path.join(MODEL_CACHE_DIR, "SenseVoiceSmall")
+PARAFORMER_LOCAL_PATH = os.path.join(MODEL_CACHE_DIR, "paraformer-zh")
+VAD_LOCAL_PATH = os.path.join(MODEL_CACHE_DIR, "fsmn-vad")
+PUNC_LOCAL_PATH = os.path.join(MODEL_CACHE_DIR, "ct-punc")
+
+os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
+
+
+def download_if_missing(repo_id, local_path, name):
+    if not os.path.exists(local_path):
+        print(f"Downloading {name}...")
+        snapshot_download(repo_id=repo_id, local_dir=local_path, ignore_patterns=["*.onnx"])
+        print(f"{name} ready.")
+    else:
+        print(f"{name} found locally.")
+
+
+download_if_missing("FunAudioLLM/SenseVoiceSmall", SENSE_VOICE_LOCAL_PATH, "SenseVoice")
+download_if_missing("funasr/paraformer-zh", PARAFORMER_LOCAL_PATH, "Paraformer-zh")
+download_if_missing("funasr/fsmn-vad", VAD_LOCAL_PATH, "FSMN-VAD")
+download_if_missing("funasr/ct-punc", PUNC_LOCAL_PATH, "CT-Punc")
+
+import gradio as gr
+import time
+import tempfile
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+loaded_models = {}
+
+
+def get_model(pipeline):
+    if pipeline in loaded_models:
+        return loaded_models[pipeline]
+
+    if pipeline == "sensevoice":
+        model = AutoModel(
+            model=SENSE_VOICE_LOCAL_PATH,
+            vad_model=VAD_LOCAL_PATH,
+            vad_kwargs={"max_single_segment_time": 30000},
+            device="cpu",
+            disable_update=True,
+            hub="hf",
+        )
+    elif pipeline == "paraformer":
+        model = AutoModel(
+            model=PARAFORMER_LOCAL_PATH,
+            vad_model=VAD_LOCAL_PATH,
+            punc_model=PUNC_LOCAL_PATH,
+            device="cpu",
+            disable_update=True,
+            hub="hf",
+        )
+    else:
+        raise ValueError(f"Unknown pipeline: {pipeline}")
+
+    loaded_models[pipeline] = model
+    return model
+
+
+def transcribe(audio_input, pipeline_type):
+    if audio_input is None:
+        return "Please upload or record audio.", ""
+
+    model = get_model(pipeline_type)
+
+    t0 = time.time()
+    if pipeline_type == "sensevoice":
+        res = model.generate(
+            input=audio_input, cache={}, language="auto",
+            use_itn=True, batch_size_s=60, merge_vad=True, merge_length_s=15,
+        )
+    else:
+        res = model.generate(input=audio_input)
+
+    text = rich_transcription_postprocess(res[0]["text"])
+    elapsed = time.time() - t0
+
+    metrics = f"Time: {elapsed:.2f}s | Model: {pipeline_type} | Device: CPU"
+    return metrics, text
+
+
+with gr.Blocks(title="FunASR Demo") as demo:
+    gr.Markdown("""
+# FunASR: Speech Recognition Demo
+
+Industrial-grade ASR toolkit. Upload audio and get transcription instantly.
+
+- **SenseVoice**: Multi-task (ASR + emotion + events), 5 languages, ultra-fast
+- **Paraformer**: Non-autoregressive Chinese ASR with punctuation
+
+[GitHub](https://github.com/modelscope/FunASR) | [Docs](https://modelscope.github.io/FunASR/) | [pip install funasr](https://pypi.org/project/funasr/)
+    """)
+
+    audio_input = gr.Audio(label="Upload or Record Audio", sources=["upload", "microphone"], type="filepath")
+
+    pipeline_type = gr.Dropdown(
+        choices=["sensevoice", "paraformer"],
+        label="Model",
+        value="sensevoice"
+    )
+
+    btn = gr.Button("Transcribe", variant="primary")
+
+    metrics_out = gr.Textbox(label="Metrics", lines=1)
+    text_out = gr.Textbox(label="Transcription", lines=8)
+
+    btn.click(transcribe, inputs=[audio_input, pipeline_type], outputs=[metrics_out, text_out])
+
+    gr.Markdown("""
+### Install & Use Locally
+```python
+pip install funasr
+from funasr import AutoModel
+model = AutoModel(model="funasr/paraformer-zh", hub="hf", vad_model="funasr/fsmn-vad", punc_model="funasr/ct-punc")
+result = model.generate(input="audio.wav")
+```
+    """)
+
+demo.queue().launch()

requirements.txt

@@ -4,4 +4,4 @@ torchaudio
 huggingface_hub
 pydub
 requests
-gradio>=5.0.0,<6.0.0
+gradio==5.23.0