gmdnn commited on
Commit
3f873d4
1 Parent(s): 261aa8b

Update app.py

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Files changed (1) hide show
  1. app.py +8 -20
app.py CHANGED
@@ -1,13 +1,5 @@
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  import os
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- os.system("pip install gradio==3.3")
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  import gradio as gr
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- import numpy as np
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-
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- title = "SpeechMatrix Speech-to-speech Translation"
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-
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- description = "Gradio Demo for SpeechMatrix. To use it, simply record your audio, or click the example to load. Read more at the links below. \nNote: These models are trained on SpeechMatrix data only, and meant to serve as a baseline for future research."
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-
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- article = "<p style='text-align: center'><a href='https://research.facebook.com/publications/speechmatrix' target='_blank'>SpeechMatrix</a> | <a href='https://github.com/facebookresearch/fairseq/tree/ust' target='_blank'>Github Repo</a></p>"
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  SRC_LIST = ['cs', 'de', 'en', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl']
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  TGT_LIST = ['en', 'fr', 'es']
@@ -16,21 +8,17 @@ for src in SRC_LIST:
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  for tgt in TGT_LIST:
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  if src != tgt:
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  MODEL_LIST.append(f"textless_sm_{src}_{tgt}")
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-
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  examples = []
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- io_dict = {model: gr.Interface.load(f"huggingface/facebook/{model}", api_key=os.environ['api_key']) for model in MODEL_LIST}
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-
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  def inference(audio, model):
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- out_audio = io_dict[model](audio)
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- return out_audio
 
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  gr.Interface(
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  inference,
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- [gr.inputs.Audio(source="microphone", type="filepath", label="Input"),gr.inputs.Dropdown(choices=MODEL_LIST, default="xm_transformer_sm_all-en",type="value", label="Model")
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  ],
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- gr.outputs.Audio(label="Output"),
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- article=article,
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- title=title,
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- examples=examples,
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- cache_examples=False,
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- description=description).queue().launch()
 
1
  import os
 
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  import gradio as gr
 
 
 
 
 
 
 
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  SRC_LIST = ['cs', 'de', 'en', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl']
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  TGT_LIST = ['en', 'fr', 'es']
 
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  for tgt in TGT_LIST:
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  if src != tgt:
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  MODEL_LIST.append(f"textless_sm_{src}_{tgt}")
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+
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  examples = []
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+ io_dict = {model: gr.load(f"huggingface/facebook/{model}") for model in MODEL_LIST}
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+
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  def inference(audio, model):
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+ out_audio = io_dict[model](audio)
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+ return out_audio
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+
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  gr.Interface(
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  inference,
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+ [gr.Audio(source="microphone", type="filepath", label="Input"),gr.Dropdown(choices=MODEL_LIST, type="value", label="Model")
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  ],
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+ gr.Audio(label="Output")).queue().launch()