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app (7).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/reverb-asr"
+
+    files = ['reverb_asr_v1.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()
+    
+
+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 (style, 1-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']
+    txt = txt.replace('▁', ' ')
+    return txt
+
+
+# input
+inputs = [
+    gr.inputs.Audio(source="microphone", type="filepath", label='Input audio'),
+    gr.Slider(0, 1, value=0, label="Verbatimicity - from non-verbatim (0) to verbatim (1)", info="Choose a transcription style between non-verbatim and verbatim"),
+]
+
+examples = [
+    ['examples/POD1000000012_S0000335.wav'],
+    ['examples/POD1000000013_S0000062.wav'],
+    ['examples/POD1000000032_S0000020.wav'], 
+    ['examples/POD1000000032_S0000038.wav'],
+    ['examples/POD1000000032_S0000050.wav'],
+    ['examples/POD1000000032_S0000058.wav'],
+]
+
+
+output = gr.outputs.Textbox(label="Output Text")
+
+text = "Reverb ASR Transcription Styles Demo"
+
+# description
+description = (
+    "Reverb ASR supports verbatim and non-verbatim transcription. Try recording an audio with disfluencies (ex: \'uh\', \'um\') and testing both transcription styles. Or, choose an example audio below."  # noqa
+)
+
+article = (
+    "<p style='text-align: center'>"
+    "<a href='https://rev.com' target='_blank'>Learn more about Rev</a>"  # noqa
+    "</p>")
+
+interface = gr.Interface(
+    fn=recognition,
+    inputs=inputs,
+    outputs=output,
+    title=text,
+    description=description,
+    article=article,
+    examples=examples,
+    theme='huggingface',
+)
+
+interface.launch(enable_queue=True)

requirements (3).txt

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+wenet @ git+https://github.com/revdotcom/reverb#subdirectory=asr
+gradio==3.14.0
+joblib~=1.4
+huggingface-hub