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# Copyright (c) 2025 SparkAudio
# 2025 Xinsheng Wang (w.xinshawn@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import soundfile as sf
import logging
import gradio as gr
from datetime import datetime
from cli.SparkTTS import SparkTTS
from sparktts.utils.token_parser import LEVELS_MAP_UI
import spaces
from huggingface_hub import snapshot_download
def initialize_model(model_dir=snapshot_download("SparkAudio/Spark-TTS-0.5B"), device=0):
"""Load the model once at the beginning."""
logging.info(f"Loading model from: {model_dir}")
device = torch.device("cuda")
model = SparkTTS(model_dir, device)
return model
@spaces.GPU
def run_tts(
text,
model,
prompt_text=None,
prompt_speech=None,
gender=None,
pitch=None,
speed=None,
save_dir="example/results",
):
"""Perform TTS inference and save the generated audio."""
logging.info(f"Saving audio to: {save_dir}")
if prompt_text is not None:
prompt_text = None if len(prompt_text) <= 1 else prompt_text
# Ensure the save directory exists
os.makedirs(save_dir, exist_ok=True)
# Generate unique filename using timestamp
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
save_path = os.path.join(save_dir, f"{timestamp}.wav")
logging.info("Starting inference...")
# Perform inference and save the output audio
with torch.no_grad():
wav = model.inference(
text,
prompt_speech,
prompt_text,
gender,
pitch,
speed,
)
sf.write(save_path, wav, samplerate=16000)
logging.info(f"Audio saved at: {save_path}")
return save_path, model # Return model along with audio path
@spaces.GPU
def voice_clone(text, model, prompt_text, prompt_wav_upload, prompt_wav_record):
"""Gradio interface for TTS with prompt speech input."""
# Determine prompt speech (from audio file or recording)
prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record
prompt_text = None if len(prompt_text) < 2 else prompt_text
audio_output_path, model = run_tts(
text, model, prompt_text=prompt_text, prompt_speech=prompt_speech
)
return audio_output_path, model
@spaces.GPU
def voice_creation(text, model, gender, pitch, speed):
"""Gradio interface for TTS with control over voice attributes."""
pitch = LEVELS_MAP_UI[int(pitch)]
speed = LEVELS_MAP_UI[int(speed)]
audio_output_path, model = run_tts(
text, model, gender=gender, pitch=pitch, speed=speed
)
return audio_output_path, model
def build_ui(model_dir, device=0):
with gr.Blocks() as demo:
# Initialize model
model = initialize_model(model_dir, device="cuda")
# Use HTML for centered title
gr.HTML('<h1 style="text-align: center;">Spark-TTS by SparkAudio</h1>')
with gr.Tabs():
# Voice Clone Tab
with gr.TabItem("Voice Clone"):
gr.Markdown(
"### Upload reference audio or recording （上传参考音频或者录音）"
)
with gr.Row():
prompt_wav_upload = gr.Audio(
sources="upload",
type="filepath",
label="Choose the prompt audio file, ensuring the sampling rate is no lower than 16kHz.",
)
prompt_wav_record = gr.Audio(
sources="microphone",
type="filepath",
label="Record the prompt audio file.",
)
with gr.Row():
text_input = gr.Textbox(
label="Text", lines=3, placeholder="Enter text here"
)
prompt_text_input = gr.Textbox(
label="Text of prompt speech (Optional; recommended for cloning in the same language.)",
lines=3,
placeholder="Enter text of the prompt speech.",
)
audio_output = gr.Audio(
label="Generated Audio", autoplay=True, streaming=True
)
generate_buttom_clone = gr.Button("Generate")
generate_buttom_clone.click(
voice_clone,
inputs=[
text_input,
gr.State(model),
prompt_text_input,
prompt_wav_upload,
prompt_wav_record,
],
outputs=[audio_output, gr.State(model)],
)
# Voice Creation Tab
with gr.TabItem("Voice Creation"):
gr.Markdown(
"### Create your own voice based on the following parameters"
)
with gr.Row():
with gr.Column():
gender = gr.Radio(
choices=["male", "female"], value="male", label="Gender"
)
pitch = gr.Slider(
minimum=1, maximum=5, step=1, value=3, label="Pitch"
)
speed = gr.Slider(
minimum=1, maximum=5, step=1, value=3, label="Speed"
)
with gr.Column():
text_input_creation = gr.Textbox(
label="Input Text",
lines=3,
placeholder="Enter text here",
value="You can generate a customized voice by adjusting parameters such as pitch and speed.",
)
create_button = gr.Button("Create Voice")
audio_output = gr.Audio(
label="Generated Audio", autoplay=True, streaming=True
)
create_button.click(
voice_creation,
inputs=[text_input_creation, gr.State(model), gender, pitch, speed],
outputs=[audio_output, gr.State(model)],
)
return demo
if __name__ == "__main__":
demo = build_ui(model_dir=snapshot_download("SparkAudio/Spark-TTS-0.5B"), device=0)
demo.queue().launch() |