| from inference.infer_tool import Svc |
| from vextract.vocal_extract import VEX |
| import gradio as gr |
| import os |
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|
| class VitsGradio: |
| def __init__(self): |
| self.so = Svc() |
| self.v = VEX() |
| self.lspk = [] |
| self.modelPaths = [] |
| for root, dirs, files in os.walk("checkpoints"): |
| for dir in dirs: |
| self.modelPaths.append(dir) |
| with gr.Blocks(title="Sovits Singing Synthesis Tool") as self.Vits: |
| gr.Markdown( |
| """ |
| # Singing Synthesis Tool |
| - Please select the voice model, device, and operating mode in sequence, then click "Load Model" |
| - The input audio needs to be clean vocals |
| """ |
| ) |
| with gr.Tab("Vocal Extraction"): |
| with gr.Row(): |
| with gr.Column(): |
| sample_audio = gr.Audio(label="Input Audio") |
| extractAudioBtn = gr.Button("Extract Vocals") |
| with gr.Row(): |
| with gr.Column(): |
| self.sample_vocal_output = gr.Audio(label="Output Audio") |
| self.sample_accompaniment_output = gr.Audio() |
| extractAudioBtn.click(self.v.separate, inputs=[sample_audio], |
| outputs=[self.sample_vocal_output, self.sample_accompaniment_output], |
| show_progress=True, api_name="extract") |
| with gr.Tab("Singing Synthesis"): |
| with gr.Row(visible=False) as self.VoiceConversion: |
| with gr.Column(): |
| with gr.Row(): |
| with gr.Column(): |
| self.srcaudio = gr.Audio(label="Input Audio") |
| self.btnVC = gr.Button("Speaker Conversion") |
| with gr.Column(): |
| with gr.Row(): |
| with gr.Column(): |
| self.dsid0 = gr.Dropdown(label="Target Character", choices=self.lspk) |
| self.tran = gr.Slider(label="Pitch Shift", maximum=60, minimum=-60, step=1, value=0) |
| self.th = gr.Slider(label="Slice Threshold", maximum=32767, minimum=-32768, step=0.1, |
| value=-40) |
| self.ns = gr.Slider(label="Noise Level", maximum=1.0, minimum=0.0, step=0.1, |
| value=0.4) |
| with gr.Row(): |
| self.VCOutputs = gr.Audio() |
| self.btnVC.click(self.so.inference, inputs=[self.srcaudio, self.dsid0, self.tran, self.th, self.ns], |
| outputs=[self.VCOutputs], show_progress=True, api_name="run") |
|
|
| with gr.Row(visible=False) as self.VoiceBatchConversion: |
| with gr.Column(): |
| with gr.Row(): |
| with gr.Column(): |
| self.srcaudio = gr.Files(label="Upload Multiple Audio Files", file_types=['.wav'], |
| interactive=True) |
| self.btnVC = gr.Button("Speaker Conversion") |
| with gr.Column(): |
| with gr.Row(): |
| with gr.Column(): |
| self.dsid1 = gr.Dropdown(label="Target Character", choices=self.lspk) |
| self.tran = gr.Slider(label="Pitch Shift", maximum=60, minimum=-60, step=1, value=0) |
| self.th = gr.Slider(label="Slice Threshold", maximum=32767, minimum=-32768, step=0.1, |
| value=-40) |
| self.ns = gr.Slider(label="Noise Level", maximum=1.0, minimum=0.0, step=0.1, |
| value=0.4) |
| with gr.Row(): |
| self.VCOutputs = gr.File(label="Output Zip File", interactive=False) |
| self.btnVC.click(self.batch_inference, inputs=[self.srcaudio, self.dsid1, self.tran, self.th, self.ns], |
| outputs=[self.VCOutputs], show_progress=True, api_name="batch") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| modelstrs = gr.Dropdown(label="Model", choices=self.modelPaths, value=self.modelPaths[0], |
| type="value") |
| devicestrs = gr.Dropdown(label="Device", choices=["cpu", "cuda"], value="cuda", type="value") |
| isbatchmod = gr.Radio(label="Operating Mode", choices=["single", "batch"], value="single", |
| info="single: Single file processing. batch: Batch processing supports uploading multiple files") |
| btnMod = gr.Button("Load Model") |
| btnMod.click(self.loadModel, inputs=[modelstrs, devicestrs, isbatchmod], |
| outputs=[self.dsid0, self.dsid1, self.VoiceConversion, self.VoiceBatchConversion], |
| show_progress=True, api_name="switch") |
|
|
| def batch_inference(self, files, chara, tran, slice_db, ns, progress=gr.Progress()): |
| from zipfile import ZipFile |
| from scipy.io import wavfile |
| import uuid |
|
|
| temp_directory = "temp" |
| if not os.path.exists(temp_directory): |
| os.mkdir(temp_directory) |
|
|
| progress(0.00, desc="Initializing Directory") |
| tmp_workdir_name = f"{temp_directory}/batch_{uuid.uuid4()}" |
| if not os.path.exists(tmp_workdir_name): |
| os.mkdir(tmp_workdir_name) |
|
|
| progress(0.10, desc="Initializing Directory") |
|
|
| output_files = [] |
|
|
| for idx, file in enumerate(files): |
| filename = os.path.basename(file.name) |
| progress(0.10 + (0.70 / float(len(files))) * (idx + 1.00), desc=f"Processing Audio {(idx + 1)}/{len(files)}: {filename}") |
| print(f"{idx}, {file}, {filename}") |
| sampling_rate, audio = wavfile.read(file.name) |
| output_sampling_rate, output_audio = self.so.inference((sampling_rate, audio), chara=chara, tran=tran, |
| slice_db=slice_db, ns=ns) |
| new_filepath = f"{tmp_workdir_name}/{filename}" |
| wavfile.write(filename=new_filepath, rate=output_sampling_rate, data=output_audio) |
| output_files.append(new_filepath) |
|
|
| progress(0.70, desc="Audio Processing Complete") |
|
|
| zipfilename = f"{tmp_workdir_name}/output.zip" |
| with ZipFile(zipfilename, "w") as zip_obj: |
| for idx, filepath in enumerate(output_files): |
| zip_obj.write(filepath, os.path.basename(filepath)) |
| progress(0.80, desc="Compression Complete") |
| |
| progress(1.00, desc="Cleaning Up") |
| return zipfilename |
|
|
| def loadModel(self, path, device, process_mode): |
| self.lspk = [] |
| print(f"path: {path}, device: {device}") |
| self.so.set_device(device) |
| print(f"device set.") |
| self.so.load_checkpoint(path) |
| print(f"checkpoint loaded") |
| for spk, sid in self.so.hps_ms.spk.items(): |
| self.lspk.append(spk) |
| print(f"LSPK: {self.lspk}") |
| if process_mode == "single": |
| VChange = gr.update(visible=True) |
| VBChange = gr.update(visible=False) |
| else: |
| VChange = gr.update(visible=False) |
| VBChange = gr.update(visible=True) |
| SD0Change = gr.update(choices=self.lspk, value=self.lspk[0]) |
| SD1Change = gr.update(choices=self.lspk, value=self.lspk[0]) |
| print("All set. Updating display") |
| return [SD0Change, SD1Change, VChange, VBChange] |
|
|
|
|
| if __name__ == "__main__": |
| grVits = VitsGradio() |
| grVits.Vits\ |
| .queue(concurrency_count=20, status_update_rate=5.0)\ |
| .launch(server_port=7870, share=True, show_api=False) |
|
|
