demo first commit

requirements.txt

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+wenet @ git+https://github.com/revdotcom/rev-wenet@feature/NERD-3007
+gradio==3.14.0

rev_app.py

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+# Copyright (c) 2022 Horizon Robotics. (authors: Binbin Zhang)
+# 2022 Chengdong Liang ([email protected])
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gradio as gr
+import torch
+from wenet.cli.model import load_model
+
+
+
+def process_cat_embs(cat_embs):
+ device = "cpu"
+ cat_embs = torch.tensor(
+ [float(c) for c in cat_embs.split(',')]).to(device)
+ return 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)
+ return model
+
+model = download_rev_models()
+ 
+
+def recognition(audio, style=0):
+ if audio is None:
+ return "Input Error! Please enter one audio!"
+ # NOTE: model supports 16k sample_rate
+
+ cat_embs = ','.join([str(s) for s in (1-style, style)])
+ cat_embs = process_cat_embs(cat_embs)
+ ans = model.transcribe(audio, cat_embs = cat_embs)
+
+ if ans is None:
+ return "ERROR! No text output! Please try again!"
+ txt = ans['text']
+ return txt
+
+
+# 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"),
+]
+
+output = gr.outputs.Textbox(label="Output Text")
+
+text = "Reginald Demo"
+
+# description
+description = (
+ "This is a speech recognition demo that supports verbatim and non-verbatim transcription. Try recording an audio with disfluencies (ex: \'uh\', \'um\') and testing both transcription styles." # noqa
+)
+
+article = (
+ "<p style='text-align: center'>"
+ "<a href='https://rev.com' target='_blank'>Github: Learn more about Rev</a>" # noqa
+ "</p>")
+
+interface = gr.Interface(
+ fn=recognition,
+ inputs=inputs,
+ outputs=output,
+ title=text,
+ description=description,
+ article=article,
+ theme='huggingface',
+)
+
+interface.launch(enable_queue=True)