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import os
import gradio as gr
import numpy as np
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.speech_to_speech.hub_interface import S2SHubInterface
from fairseq.models.speech_to_text.hub_interface import S2THubInterface
from audio_pipe import SpeechToSpeechPipeline
io1 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_en-hk", api_key=os.environ['api_key'])
io2 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_hk-en", api_key=os.environ['api_key'])
io3 = gr.Interface.load("huggingface/facebook/xm_transformer_unity_en-hk", api_key=os.environ['api_key'])
io4 = gr.Interface.load("huggingface/facebook/xm_transformer_unity_hk-en", api_key=os.environ['api_key'])
pipe = SpeechToSpeechPipeline("facebook/xm_transformer_unity_hk-en")
def call_model(audio, model):
# pipe = SpeechToSpeechPipeline("facebook/xm_transformer_unity_hk-en")
# wav, sr, text = pipe(audio)
temp_file = pipe(audio)
return gr.Audio(temp_file)
def inference(audio, model):
if model == "xm_transformer_s2ut_en-hk":
out_audio = io1(audio)
elif model == "xm_transformer_s2ut_hk-en":
out_audio = io2(audio)
elif model == "xm_transformer_unity_en-hk":
out_audio = io3(audio)
elif model == "xm_transformer_unity_hk-en_gpu":
out_audio = call_model(audio, model)
else:
out_audio = io4(audio)
return out_audio
css = """
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
color: black;
border-color: grey;
background: white;
}
input[type='range'] {
accent-color: black;
}
.dark input[type='range'] {
accent-color: #dfdfdf;
}
.container {
max-width: 730px;
margin: auto;
padding-top: 1.5rem;
}
.details:hover {
text-decoration: underline;
}
.gr-button {
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
.footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.prompt h4{
margin: 1.25em 0 .25em 0;
font-weight: bold;
font-size: 115%;
}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
"""
block = gr.Blocks(css=css)
with block:
gr.HTML(
"""
<div style="text-align: center; max-width: 700px; margin: 0 auto;">
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
<h1 style="font-weight: 900; margin-bottom: 7px;">
Hokkien Translation
</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
A demo for fairseq speech-to-speech translation models. It supports S2UT and UnitY models for bidirectional Hokkien and English translation. Please select the model and record the input to submit.
</p>
</div>
"""
)
with gr.Group():
with gr.Box():
with gr.Row().style(mobile_collapse=False, equal_height=True):
audio = gr.Audio(
source="microphone", type="filepath", label="Input"
)
btn = gr.Button("Submit")
model = gr.Dropdown(choices=["xm_transformer_unity_en-hk", "xm_transformer_unity_hk-en", "xm_transformer_unity_hk-en_gpu", "xm_transformer_s2ut_en-hk", "xm_transformer_s2ut_hk-en"], value="xm_transformer_unity_en-hk",type="value", label="Model")
out = gr.Audio(label="Output")
btn.click(inference, inputs=[audio, model], outputs=out)
gr.HTML('''
<div class="footer">
<p>Model by <a href="https://ai.facebook.com/" style="text-decoration: underline;" target="_blank">Meta AI</a>
</p>
</div>
''')
block.launch() |