get model from huggingface-hub

app.py

@@ -27,22 +27,14 @@ def process_cat_embs(cat_embs):
 
 
 def download_rev_models():
- # from huggingface_hub import hf_hub_download
- # import joblib
-
- # REPO_ID = "Revai/reginald"
- # FILENAME = "sklearn_model.joblib"
-
- # model = joblib.load(
- # hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
- # )
- model_path = "/Users/natalie/NERD-2941/reginald/10.jit.zip"
- units_path = "/Users/natalie/NERD-2941/reginald/tk.units.txt"
- audio_path = "/Users/natalie/NERD-2941/rev-wenet/runtime/web/fdhc0_si1559.wav"
- cat_embs = "1,0"
- device = "cpu"
- cat_embs = process_cat_embs
- model = load_model(model_path, units_path)
+ from huggingface_hub import hf_hub_download
+ import joblib
+
+ REPO_ID = "Revai/reginald"
+
+ files = ['10.jit.zip', 'tk.units.txt']
+ downloaded_files = [hf_hub_download(repo_id=REPO_ID, filename=f) for f in files]
+ model = load_model(downloaded_files[0], downloaded_files[1])
  return model
 
 model = download_rev_models()
@@ -66,7 +58,7 @@ def recognition(audio, style=0):
 # input
 inputs = [
  gr.inputs.Audio(source="microphone", type="filepath", label='Input audio'),
- gr.Slider(0, 1, value=0, label="Style", info="Choose between verbatim and NV"),
+ gr.Slider(0, 1, value=0, label="Style - from verbatim (0) to non-verbatim (1)", info="Choose between verbatim and NV"),
 ]
 
 output = gr.outputs.Textbox(label="Output Text")

requirements.txt

@@ -1,2 +1,3 @@
 wenet @ git+https://github.com/revdotcom/rev-wenet@feature/NERD-3007
 gradio==3.14.0
+huggingface-hub