feat: upload inference script

app.py

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+import os
+
+import gradio as gr
+import torch
+from huggingface_hub import snapshot_download
+from nemo.collections.asr.models import ASRModel
+from omegaconf import OmegaConf
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+
+def load_model(model_id: str):
+    model_dir = snapshot_download(model_id)
+    model_ckpt_path = os.path.join(model_dir, "model.nemo")
+    asr_model = ASRModel.restore_from(model_ckpt_path)
+    asr_model.eval()
+    asr_model = asr_model.to(device)
+
+    return asr_model
+
+
+OmegaConf.register_new_resolver("load_model", load_model)
+
+models_config = OmegaConf.to_object(OmegaConf.load("configs/models.yaml"))
+
+
+def automatic_speech_recognition(model_id: str, audio_file: str):
+    model = models_config[model_id]["model"]
+    text = model.transcribe(audio_file)
+    return text
+
+
+demo = gr.Blocks(
+    title="康統語音辨識系統",
+)
+
+with demo:
+    default_model_id = list(models_config.keys())[0]
+    model_drop_down = gr.Dropdown(
+        models_config.keys(),
+        value=default_model_id,
+        label="模型",
+    )
+
+    gr.Markdown(
+        """
+        # 康統語音辨識系統
+        """
+    )
+    gr.Interface(
+        automatic_speech_recognition,
+        inputs=[
+            model_drop_down,
+            gr.Audio(
+                label="上傳或錄音",
+                type="filepath",
+                waveform_options=gr.WaveformOptions(
+                    sample_rate=16000,
+                ),
+            ),
+        ],
+        outputs=[
+            gr.Text(interactive=False, label="辨識結果"),
+        ],
+        allow_flagging="auto",
+    )
+
+demo.launch()

configs/models.yaml

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+parakeet-0.6b-custom-tokenizer:
+  model: ${load_model:kenkone/parakeet-ctc-0.6b-custom-tokenizer}

requirements.txt

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+omegaconf
+git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]
+IPython
+numpy==1.*