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import gradio as gr
import os
import httpx
import numpy as np
import base64
import torch
import torchaudio
import io
URL = os.environ['TEMP_HOSTING_URL']
API_KEY = os.environ['TEMP_CALLING_KEY']
def inference(reference_audio, text, reference_text, ras_K, ras_t_r, top_p, quality_prefix, clone_method):
_sr, _wav = reference_audio
wav = torch.from_numpy(_wav).float()
wav = wav / 32768.0
if wav.dim() == 1: wav = wav[None]
else:
wav = wav.mean(dim=-1)[None]
wav = torchaudio.functional.resample(wav, _sr, 24000)
io_data = io.BytesIO()
torchaudio.save(io_data, wav, sample_rate=24000, format='wav')
io_data.seek(0)
encoded_data = base64.b64encode(io_data.read())
encoded_str = encoded_data.decode("utf-8")
if clone_method == 'deep-clone':
dlc = 'fixed-ref'
elif clone_method == 'shallow-clone':
dlc = 'none'
elif clone_method == 'follow-on deep-clone':
dlc = 'per-chunk'
data = {
"text": text,
"reference_audio": encoded_str, # reference audio, b64 encoded. Should be <=15s.
"reference_text": reference_text if reference_text is not None and len(reference_text) > 0 else None,
"language": 'en-us',
"inference_settings": {'top_p': top_p, "prefix": quality_prefix, 'ras_K': ras_K, 'ras_t_r': ras_t_r, 'deep_clone_mode': dlc},
}
print(f"Calling with payload {data['inference_settings']}")
# Send the POST request
headers={"Authorization": f"Api-Key {API_KEY}"}
response = httpx.post(URL, headers=headers, json=data, timeout=300)
# Check the response status code
if response.status_code == 200: print("Request successful!")
else: print("Request failed with status code", response.status_code)
full_audio_bytes = base64.b64decode(response.json()['output'])
wav, sr = torchaudio.load(io.BytesIO(full_audio_bytes))
wav = wav.numpy()
return (sr, wav.T)
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown("## Reference Audio")
with gr.Row():
reference_audio = gr.Audio(label="Drop Audio Here", max_length=16)
with gr.Row():
gr.Markdown("## Text to Generate")
with gr.Row():
text_input = gr.Textbox(label="Text to Generate")
with gr.Row():
synthesize_button = gr.Button("Synthesize", variant="primary")
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
reference_text = gr.Textbox(label="Reference Text",
info="Leave blank to automatically transcribe the reference audio. Inference will be slightly faster if you specify the correct reference transcript below.")
with gr.Row():
ras_K = gr.Slider(minimum=1, maximum=20, step=1, value=10, label="RAS_K", info="RAS sampling K value")
with gr.Row():
ras_t_r = gr.Slider(minimum=0.001, maximum=1, step=0.001, value=0.09, label="RAS_t_r", info="RAS sampling t_r value")
with gr.Row():
top_p = gr.Slider(minimum=0.001, maximum=1, step=0.001, value=0.2, label="top_p", info="top-p sampling value")
with gr.Row():
quality_prefix = gr.Textbox('48000', label="quality_prefix", info="quality prefix string to append to generation", lines=1)
with gr.Row():
gr.Markdown("Cloning method to use. Deep clone and shallow clone use the method described in the paper, " +
"while `follow-on deep clone` uses deep cloning, but always using the previous generated segment as the deep clone conditioning. " +
"This only makes a difference for long text inputs where the text is internally chunked up and generated in chunks.")
clone_method = gr.Radio(choices=['deep-clone', 'shallow-clone', 'follow-on deep-clone'], value='deep-clone', label="cloning method", info="cloning method to use")
with gr.Row():
gr.Markdown("## Synthesized Audio")
with gr.Row():
audio_output = gr.Audio(label="Synthesized Audio")
synthesize_button.click(
inference,
inputs=[reference_audio, text_input, reference_text, ras_K, ras_t_r, top_p, quality_prefix, clone_method],
outputs=[audio_output]
)
if __name__ == "__main__":
demo.launch(share=False)
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