sanchit-gandhi HF staff commited on
Commit
d101dad
1 Parent(s): 0a1595f

add second example

Browse files
Files changed (2) hide show
  1. app.py +19 -5
  2. assets/sample_input_2.mp3 +3 -0
app.py CHANGED
@@ -1,5 +1,6 @@
1
  import os
2
 
 
3
  import gradio as gr
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  import numpy as np
5
  import torch
@@ -48,12 +49,12 @@ translator = Translator(
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  def predict(
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  task_name: str,
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  audio_source: str,
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- input_audio_mic: str | None,
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- input_audio_file: str | None,
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- input_text: str | None,
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- source_language: str | None,
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  target_language: str,
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- ) -> tuple[tuple[int, np.ndarray] | None, str]:
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  task_name = task_name.split()[0]
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  source_language_code = LANGUAGE_NAME_TO_CODE.get(source_language, None)
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  target_language_code = LANGUAGE_NAME_TO_CODE[target_language]
@@ -290,6 +291,8 @@ with gr.Blocks(css="style.css") as demo:
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  examples=[
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  ["assets/sample_input.mp3", "French"],
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  ["assets/sample_input.mp3", "Mandarin Chinese"],
 
 
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  ],
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  inputs=[input_audio_file, target_language],
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  outputs=[output_audio, output_text],
@@ -301,6 +304,8 @@ with gr.Blocks(css="style.css") as demo:
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  examples=[
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  ["assets/sample_input.mp3", "French"],
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  ["assets/sample_input.mp3", "Mandarin Chinese"],
 
 
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  ],
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  inputs=[input_audio_file, target_language],
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  outputs=[output_audio, output_text],
@@ -312,6 +317,10 @@ with gr.Blocks(css="style.css") as demo:
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  examples=[
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  ["My favorite animal is the elephant.", "English", "French"],
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  ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
 
 
 
 
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  ],
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  inputs=[input_text, source_language, target_language],
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  outputs=[output_audio, output_text],
@@ -323,6 +332,10 @@ with gr.Blocks(css="style.css") as demo:
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  examples=[
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  ["My favorite animal is the elephant.", "English", "French"],
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  ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
 
 
 
 
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  ],
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  inputs=[input_text, source_language, target_language],
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  outputs=[output_audio, output_text],
@@ -333,6 +346,7 @@ with gr.Blocks(css="style.css") as demo:
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  asr_examples = gr.Examples(
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  examples=[
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  ["assets/sample_input.mp3", "English"],
 
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  ],
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  inputs=[input_audio_file, target_language],
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  outputs=[output_audio, output_text],
 
1
  import os
2
 
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+ from typing import Union
4
  import gradio as gr
5
  import numpy as np
6
  import torch
 
49
  def predict(
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  task_name: str,
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  audio_source: str,
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+ input_audio_mic: Union[str, None],
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+ input_audio_file: Union[str, None],
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+ input_text: Union[str, None],
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+ source_language: Union[str, None],
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  target_language: str,
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+ ) -> tuple[Union[tuple[int, np.ndarray], None], str]:
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  task_name = task_name.split()[0]
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  source_language_code = LANGUAGE_NAME_TO_CODE.get(source_language, None)
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  target_language_code = LANGUAGE_NAME_TO_CODE[target_language]
 
291
  examples=[
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  ["assets/sample_input.mp3", "French"],
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  ["assets/sample_input.mp3", "Mandarin Chinese"],
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+ ["assets/sample_input_2.mp3", "Hindi"],
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+ ["assets/sample_input_2.mp3", "Spanish"],
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  ],
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  inputs=[input_audio_file, target_language],
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  outputs=[output_audio, output_text],
 
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  examples=[
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  ["assets/sample_input.mp3", "French"],
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  ["assets/sample_input.mp3", "Mandarin Chinese"],
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+ ["assets/sample_input_2.mp3", "Hindi"],
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+ ["assets/sample_input_2.mp3", "Spanish"],
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  ],
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  inputs=[input_audio_file, target_language],
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  outputs=[output_audio, output_text],
 
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  examples=[
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  ["My favorite animal is the elephant.", "English", "French"],
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  ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
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+ ["Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
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+ "English", "Hindi"],
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+ ["Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
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+ "English", "Spanish"],
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  ],
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  inputs=[input_text, source_language, target_language],
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  outputs=[output_audio, output_text],
 
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  examples=[
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  ["My favorite animal is the elephant.", "English", "French"],
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  ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
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+ ["Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
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+ "English", "Hindi"],
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+ ["Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
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+ "English", "Spanish"],
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  ],
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  inputs=[input_text, source_language, target_language],
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  outputs=[output_audio, output_text],
 
346
  asr_examples = gr.Examples(
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  examples=[
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  ["assets/sample_input.mp3", "English"],
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+ ["assets/sample_input_2.mp3", "English"],
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  ],
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  inputs=[input_audio_file, target_language],
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  outputs=[output_audio, output_text],
assets/sample_input_2.mp3 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6a505a4641e3f5f0ddec9508832793aa20e63d2545530b66bc04a9bd19a742e6
3
+ size 30624