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