File size: 3,761 Bytes
66baa21 ab40649 66baa21 ff3710a 66baa21 ff3710a 66baa21 ff3710a 66baa21 5eb978f 66baa21 3857b9c 66baa21 e69710d 66baa21 ab40649 66baa21 f52ccf3 66baa21 f922a17 66baa21 5eb978f 66baa21 f52ccf3 66baa21 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
import torch
from transformers import pipeline
import numpy as np
import gradio as gr
def _grab_best_device(use_gpu=True):
if torch.cuda.device_count() > 0 and use_gpu:
device = "cuda"
else:
device = "cpu"
return device
device = _grab_best_device()
default_model_per_language = {
"marathi": "ylacombe/mms-mar-finetuned-monospeaker"
}
models_per_language = {
"marathi": ["ylacombe/mms-mar-finetuned-monospeaker"]
}
HUB_PATH = "ylacombe/mms-mar-finetuned-monospeaker"
pipe_dict = {
"current_model": "ylacombe/mms-mar-finetuned-monospeaker",
"pipe": pipeline("text-to-speech", model=HUB_PATH, device=0),
"original_pipe": pipeline("text-to-speech", model=default_model_per_language["marathi"], device=0),
"language": "marathi",
}
title = """
Marathi Parkinson Enabler: Speaking is a big challenge during Parakinsons. Patients show slurred speech and cannot communicate effectively.
This is marathi text to speech model for parkinson users who want to communicate in Marathi.
"""
max_speakers = 1
# Inference
def generate_audio(text, model_id, language):
if pipe_dict["language"] != language:
gr.Warning(f"Language has changed - loading new default model: {default_model_per_language[language]}")
pipe_dict["language"] = language
pipe_dict["original_pipe"] = pipeline("text-to-speech", model=default_model_per_language[language], device=0)
num_speakers = pipe_dict["pipe"].model.config.num_speakers
out = []
output = pipe_dict["original_pipe"](text)
output = gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=True, label=f"Finetuned model prediction {default_model_per_language[language]}", show_label=True,
visible=True)
return output
css = """
#container{
margin: 0 auto;
max-width: 80rem;
}
#intro{
max-width: 100%;
text-align: center;
margin: 0 auto;
}
"""
# Gradio blocks demo
with gr.Blocks(css=css) as demo_blocks:
gr.Markdown(title, elem_id="intro")
with gr.Row():
with gr.Column():
inp_text = gr.Textbox(label="Input Text", info="What sentence would you like to synthesise?")
btn = gr.Button("Generate Audio!")
language = gr.Dropdown(
default_model_per_language.keys(),
value = "marathi",
label = "language",
info = "Language that you want to test"
)
model_id = gr.Dropdown(
models_per_language["marathi"],
value="ylacombe/mms-mar-finetuned-monospeaker",
label="Model",
info="Model you want to test",
)
with gr.Column():
output = gr.Audio(type="numpy", autoplay=False, label=f"Generated Audio", show_label=True, visible=False)
with gr.Accordion("Datasets and models details", open=False):
gr.Markdown("""
### Marathi
* **Model**: [Marathi MMS TTS](https://huggingface.co/facebook/mms-tts-mar).
* **Datasets**:
- [Marathi TTS dataset](https://huggingface.co/datasets/ylacombe/google-chilean-marathi).
""")
language.change(lambda language: gr.Dropdown(
models_per_language[language],
value=models_per_language[language][0],
label="Model",
info="Model you want to test",
),
language,
model_id
)
btn.click(generate_audio, [inp_text, model_id, language], output)
demo_blocks.queue().launch() |