mirror of
https://github.com/w-okada/voice-changer.git
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Add chihaya_jinja_sample
Web Edition improvement(16k test) bugfix: - merge slot - servermode append error
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w-okada
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vendored
@ -58,6 +58,9 @@ server/samples_0003_o.json
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server/samples_0003_t2.json
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server/samples_0003_o2.json
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server/samples_0003_d2.json
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server/samples_0004_t.json
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server/samples_0004_o.json
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server/samples_0004_d.json
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server/test_official_v1_v2.json
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server/test_ddpn_v1_v2.json
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@ -45,6 +45,7 @@ export type WebInfoStateAndMethod = WebInfoState & {
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const ModelSampleRateStr = {
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"40k": "40k",
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"32k": "32k",
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"16k": "16k",
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} as const;
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type ModelSampleRateStr = (typeof ModelSampleRateStr)[keyof typeof ModelSampleRateStr];
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@ -71,18 +72,22 @@ const noF0ModelUrl: { [modelType in VoiceChangerType]: { [inputLength in InputLe
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"24000": {
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"40k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_40k_nof0_24000.bin",
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"32k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_32k_nof0_24000.bin",
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"16k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_16k_nof0_24000.bin",
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},
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"16000": {
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"40k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_40k_nof0_16000.bin",
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"32k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_32k_nof0_16000.bin",
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"16k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_16k_nof0_16000.bin",
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},
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"12000": {
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"40k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_40k_nof0_12000.bin",
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"32k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_32k_nof0_12000.bin",
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"16k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_16k_nof0_12000.bin",
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},
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"8000": {
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"40k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_40k_nof0_8000.bin",
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"32k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_32k_nof0_8000.bin",
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"16k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_16k_nof0_8000.bin",
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},
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},
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};
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@ -109,18 +114,22 @@ const f0ModelUrl: { [modelType in VoiceChangerType]: { [inputLength in InputLeng
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"24000": {
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"40k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_40k_f0_24000.bin",
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"32k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_32k_f0_24000.bin",
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"16k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_16k_f0_24000.bin",
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},
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"16000": {
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"40k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_40k_f0_16000.bin",
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"32k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_32k_f0_16000.bin",
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"16k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_16k_f0_16000.bin",
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},
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"12000": {
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"40k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_40k_f0_12000.bin",
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"32k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_32k_f0_12000.bin",
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"16k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_16k_f0_12000.bin",
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},
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"8000": {
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"40k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_40k_f0_8000.bin",
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"32k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_32k_f0_8000.bin",
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"16k": "https://huggingface.co/wok000/vcclient_model/resolve/main/web_model/v_01_alpha/amitaro/rvcv2_amitaro_v2_16k_f0_8000.bin",
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},
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},
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};
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@ -50,7 +50,7 @@ export const Portrait = (_props: PortraitProps) => {
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}
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vol.innerText = volume.toFixed(4);
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if (webEdition) {
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buf.innerText = webInfoState.responseTimeInfo.realDuration.toString() ?? "0";
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buf.innerText = bufferingTime.toString();
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res.innerText = webInfoState.responseTimeInfo.responseTime.toString() ?? "0";
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rtf.innerText = webInfoState.responseTimeInfo.rtf.toString() ?? "0";
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} else {
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@ -55,6 +55,7 @@ export const WebEditionSettingArea = (_props: WebEditionSettingAreaProps) => {
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</div>
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);
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const sr16KClassName = "character-area-control-button" + (webInfoState.voiceChangerConfig.sampleRate == "16k" ? " character-area-control-button-active" : " character-area-control-button-stanby");
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const sr32KClassName = "character-area-control-button" + (webInfoState.voiceChangerConfig.sampleRate == "32k" ? " character-area-control-button-active" : " character-area-control-button-stanby");
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const sr40KClassName = "character-area-control-button" + (webInfoState.voiceChangerConfig.sampleRate == "40k" ? " character-area-control-button-active" : " character-area-control-button-stanby");
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const sampleRate = (
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@ -64,6 +65,15 @@ export const WebEditionSettingArea = (_props: WebEditionSettingAreaProps) => {
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<div className="character-area-slider-control">
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<span className="character-area-slider-control-kind"></span>
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<span className="character-area-control-buttons">
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<span
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className={!readyForConfig ? "character-area-control-button-disable" : sr16KClassName}
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onClick={() => {
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if (webInfoState.voiceChangerConfig.sampleRate == "16k" || !readyForConfig) return;
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webInfoState.setVoiceChangerConfig("rvcv2", "16k", webInfoState.voiceChangerConfig.useF0, webInfoState.voiceChangerConfig.inputLength);
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}}
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>
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16k
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</span>
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<span
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className={!readyForConfig ? "character-area-control-button-disable" : sr32KClassName}
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onClick={() => {
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@ -98,11 +98,9 @@ RVCSampleMode: TypeAlias = Literal[
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def getSampleJsonAndModelIds(mode: RVCSampleMode):
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if mode == "production":
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return [
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# "https://huggingface.co/wok000/vcclient_model/raw/main/samples_0001.json",
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# "https://huggingface.co/wok000/vcclient_model/raw/main/samples_0002.json",
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"https://huggingface.co/wok000/vcclient_model/raw/main/samples_0003_t2.json",
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"https://huggingface.co/wok000/vcclient_model/raw/main/samples_0003_o2.json",
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"https://huggingface.co/wok000/vcclient_model/raw/main/samples_0003_d2.json",
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"https://huggingface.co/wok000/vcclient_model/raw/main/samples_0004_t.json",
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"https://huggingface.co/wok000/vcclient_model/raw/main/samples_0004_o.json",
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"https://huggingface.co/wok000/vcclient_model/raw/main/samples_0004_d.json",
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], [
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("Tsukuyomi-chan_o", {"useIndex": False}),
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("Amitaro_o", {"useIndex": False}),
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@ -7,7 +7,7 @@ from voice_changer.DiffusionSVC.inferencer.diffusion_svc_model.diffusion.vocoder
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from voice_changer.DiffusionSVC.inferencer.onnx.VocoderOnnx import VocoderOnnx
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from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
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from voice_changer.utils.Timer import Timer
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from voice_changer.utils.Timer import Timer2
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class DiffusionSVCInferencer(Inferencer):
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@ -49,18 +49,14 @@ class DiffusionSVCInferencer(Inferencer):
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return model_block_size, model_sampling_rate
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@torch.no_grad() # 最基本推理代码,将输入标准化为tensor,只与mel打交道
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def __call__(self, units, f0, volume, spk_id=1, spk_mix_dict=None, aug_shift=0,
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gt_spec=None, infer_speedup=10, method='dpm-solver', k_step=None, use_tqdm=True,
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spk_emb=None):
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def __call__(self, units, f0, volume, spk_id=1, spk_mix_dict=None, aug_shift=0, gt_spec=None, infer_speedup=10, method="dpm-solver", k_step=None, use_tqdm=True, spk_emb=None):
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if self.diff_args.model.k_step_max is not None:
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if k_step is None:
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raise ValueError("k_step must not None when Shallow Diffusion Model inferring")
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if k_step > int(self.diff_args.model.k_step_max):
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raise ValueError("k_step must <= k_step_max of Shallow Diffusion Model")
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if gt_spec is None:
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raise ValueError("gt_spec must not None when Shallow Diffusion Model inferring, gt_spec can from "
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"input mel or output of naive model")
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raise ValueError("gt_spec must not None when Shallow Diffusion Model inferring, gt_spec can from " "input mel or output of naive model")
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aug_shift = torch.from_numpy(np.array([[float(aug_shift)]])).float().to(self.dev)
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@ -75,8 +71,7 @@ class DiffusionSVCInferencer(Inferencer):
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return self.diff_model(units, f0, volume, spk_id=spk_id, spk_mix_dict=spk_mix_dict, aug_shift=aug_shift, gt_spec=gt_spec, infer=True, infer_speedup=infer_speedup, method=method, k_step=k_step, use_tqdm=use_tqdm, spk_emb=spk_emb, spk_emb_dict=spk_emb_dict)
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@torch.no_grad()
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def naive_model_call(self, units, f0, volume, spk_id=1, spk_mix_dict=None,
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aug_shift=0, spk_emb=None):
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def naive_model_call(self, units, f0, volume, spk_id=1, spk_mix_dict=None, aug_shift=0, spk_emb=None):
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# spk_id
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spk_emb_dict = None
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if self.diff_args.model.use_speaker_encoder: # with speaker encoder
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@ -85,9 +80,7 @@ class DiffusionSVCInferencer(Inferencer):
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else:
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spk_id = torch.LongTensor(np.array([[int(spk_id)]])).to(self.dev)
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aug_shift = torch.from_numpy(np.array([[float(aug_shift)]])).float().to(self.dev)
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out_spec = self.naive_model(units, f0, volume, spk_id=spk_id, spk_mix_dict=spk_mix_dict,
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aug_shift=aug_shift, infer=True,
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spk_emb=spk_emb, spk_emb_dict=spk_emb_dict)
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out_spec = self.naive_model(units, f0, volume, spk_id=spk_id, spk_mix_dict=spk_mix_dict, aug_shift=aug_shift, infer=True, spk_emb=spk_emb, spk_emb_dict=spk_emb_dict)
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return out_spec
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@torch.no_grad()
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@ -114,19 +107,18 @@ class DiffusionSVCInferencer(Inferencer):
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silence_front: float,
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skip_diffusion: bool = True,
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) -> torch.Tensor:
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with Timer("pre-process", False) as t:
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with Timer2("pre-process", False) as t:
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gt_spec = self.naive_model_call(feats, pitch, volume, spk_id=sid, spk_mix_dict=None, aug_shift=0, spk_emb=None)
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# print("[ ----Timer::1: ]", t.secs)
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with Timer("pre-process", False) as t:
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with Timer2("pre-process", False) as t:
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if skip_diffusion == 0:
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out_mel = self.__call__(feats, pitch, volume, spk_id=sid, spk_mix_dict=None, aug_shift=0, gt_spec=gt_spec, infer_speedup=infer_speedup, method='dpm-solver', k_step=k_step, use_tqdm=False, spk_emb=None)
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out_mel = self.__call__(feats, pitch, volume, spk_id=sid, spk_mix_dict=None, aug_shift=0, gt_spec=gt_spec, infer_speedup=infer_speedup, method="dpm-solver", k_step=k_step, use_tqdm=False, spk_emb=None)
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gt_spec = out_mel
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# print("[ ----Timer::2: ]", t.secs)
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with Timer("pre-process", False) as t: # NOQA
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with Timer2("pre-process", False) as t: # NOQA
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if self.vocoder_onnx is None:
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start_frame = int(silence_front * self.vocoder.vocoder_sample_rate / self.vocoder.vocoder_hop_size)
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out_wav = self.mel2wav(gt_spec, pitch, start_frame=start_frame)
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@ -17,7 +17,7 @@ from voice_changer.RVC.embedder.Embedder import Embedder
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from voice_changer.common.VolumeExtractor import VolumeExtractor
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from torchaudio.transforms import Resample
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from voice_changer.utils.Timer import Timer
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from voice_changer.utils.Timer import Timer2
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logger = VoiceChangaerLogger.get_instance().getLogger()
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@ -45,7 +45,7 @@ class Pipeline(object):
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device,
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isHalf,
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resamplerIn: Resample,
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resamplerOut: Resample
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resamplerOut: Resample,
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):
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self.inferencer = inferencer
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inferencer_block_size, inferencer_sampling_rate = inferencer.getConfig()
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@ -64,7 +64,7 @@ class Pipeline(object):
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logger.info("GENERATE INFERENCER" + str(self.inferencer))
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logger.info("GENERATE EMBEDDER" + str(self.embedder))
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logger.info("GENERATE PITCH EXTRACTOR" + str(self.pitchExtractor))
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self.targetSR = targetSR
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self.device = device
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self.isHalf = False
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@ -103,7 +103,7 @@ class Pipeline(object):
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skip_diffusion=True,
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):
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# print("---------- pipe line --------------------")
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with Timer("pre-process", False) as t:
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with Timer2("pre-process", False) as t:
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audio_t = torch.from_numpy(audio).float().unsqueeze(0).to(self.device)
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audio16k = self.resamplerIn(audio_t)
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volume, mask = self.extract_volume_and_mask(audio16k, threshold=-60.0)
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@ -111,7 +111,7 @@ class Pipeline(object):
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n_frames = int(audio16k.size(-1) // self.hop_size + 1)
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# print("[Timer::1: ]", t.secs)
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with Timer("pre-process", False) as t:
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with Timer2("pre-process", False) as t:
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# ピッチ検出
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try:
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# pitch = self.pitchExtractor.extract(
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@ -141,8 +141,7 @@ class Pipeline(object):
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feats = feats.view(1, -1)
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# print("[Timer::2: ]", t.secs)
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with Timer("pre-process", False) as t:
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with Timer2("pre-process", False) as t:
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# embedding
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with autocast(enabled=self.isHalf):
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try:
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@ -156,28 +155,17 @@ class Pipeline(object):
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raise DeviceChangingException()
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else:
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raise e
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feats = F.interpolate(feats.permute(0, 2, 1), size=int(n_frames), mode='nearest').permute(0, 2, 1)
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feats = F.interpolate(feats.permute(0, 2, 1), size=int(n_frames), mode="nearest").permute(0, 2, 1)
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# print("[Timer::3: ]", t.secs)
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with Timer("pre-process", False) as t:
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with Timer2("pre-process", False) as t:
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# 推論実行
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try:
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with torch.no_grad():
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with autocast(enabled=self.isHalf):
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audio1 = (
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torch.clip(
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self.inferencer.infer(
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audio16k,
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feats,
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pitch.unsqueeze(-1),
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volume,
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mask,
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sid,
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k_step,
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infer_speedup,
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silence_front=silence_front,
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skip_diffusion=skip_diffusion
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).to(dtype=torch.float32),
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self.inferencer.infer(audio16k, feats, pitch.unsqueeze(-1), volume, mask, sid, k_step, infer_speedup, silence_front=silence_front, skip_diffusion=skip_diffusion).to(dtype=torch.float32),
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-1.0,
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1.0,
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)
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@ -191,7 +179,7 @@ class Pipeline(object):
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raise e
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# print("[Timer::4: ]", t.secs)
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with Timer("pre-process", False) as t: # NOQA
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with Timer2("pre-process", False) as t: # NOQA
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feats_buffer = feats.squeeze(0).detach().cpu()
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if pitch is not None:
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pitch_buffer = pitch.squeeze(0).detach().cpu()
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@ -9,7 +9,7 @@ from mods.log_control import VoiceChangaerLogger
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from voice_changer.Local.AudioDeviceList import checkSamplingRate, list_audio_device
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import time
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import sounddevice as sd
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from voice_changer.utils.Timer import Timer
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from voice_changer.utils.Timer import Timer2
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import librosa
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from voice_changer.utils.VoiceChangerModel import AudioInOut
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@ -139,7 +139,7 @@ class ServerDevice:
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return out_wav, times
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def _processDataWithTime(self, indata: np.ndarray):
|
||||
with Timer("all_inference_time") as t:
|
||||
with Timer2("all_inference_time", False) as t:
|
||||
out_wav, times = self._processData(indata)
|
||||
all_inference_time = t.secs
|
||||
self.performance = [all_inference_time] + times
|
||||
|
||||
@ -364,7 +364,7 @@ class VoiceChangerManager(ServerDeviceCallbacks):
|
||||
req = json.loads(request)
|
||||
req = ModelMergerRequest(**req)
|
||||
req.files = [MergeElement(**f) for f in req.files]
|
||||
slot = len(self.modelSlotManager.getAllSlotInfo()) - 1
|
||||
slot = len(self.modelSlotManager.getAllSlotInfo()) - 2 # Beatrice-JVS が追加されたので -1 -> -2
|
||||
if req.voiceChangerType == "RVC":
|
||||
merged = RVCModelMerger.merge_models(self.params, req, slot)
|
||||
loadParam = LoadModelParams(voiceChangerType="RVC", slot=slot, isSampleMode=False, sampleId="", files=[LoadModelParamFile(name=os.path.basename(merged), kind="rvcModel", dir="")], params={})
|
||||
|
||||
@ -3,45 +3,45 @@ import inspect
|
||||
from typing import Dict, List
|
||||
|
||||
|
||||
class Timer(object):
|
||||
storedSecs: Dict[str, Dict[str, List[float]]] = {} # Class variable
|
||||
# class Timer(object):
|
||||
# storedSecs: Dict[str, Dict[str, List[float]]] = {} # Class variable
|
||||
|
||||
def __init__(self, title: str, enalbe: bool = True):
|
||||
self.title = title
|
||||
self.enable = enalbe
|
||||
self.secs = 0
|
||||
self.msecs = 0
|
||||
self.avrSecs = 0
|
||||
# def __init__(self, title: str, enalbe: bool = True):
|
||||
# self.title = title
|
||||
# self.enable = enalbe
|
||||
# self.secs = 0
|
||||
# self.msecs = 0
|
||||
# self.avrSecs = 0
|
||||
|
||||
if self.enable is False:
|
||||
return
|
||||
# if self.enable is False:
|
||||
# return
|
||||
|
||||
self.maxStores = 10
|
||||
# self.maxStores = 10
|
||||
|
||||
current_frame = inspect.currentframe()
|
||||
caller_frame = inspect.getouterframes(current_frame, 2)
|
||||
frame = caller_frame[1]
|
||||
filename = frame.filename
|
||||
line_number = frame.lineno
|
||||
self.key = f"{title}_{filename}_{line_number}"
|
||||
if self.key not in self.storedSecs:
|
||||
self.storedSecs[self.key] = {}
|
||||
# current_frame = inspect.currentframe()
|
||||
# caller_frame = inspect.getouterframes(current_frame, 2)
|
||||
# frame = caller_frame[1]
|
||||
# filename = frame.filename
|
||||
# line_number = frame.lineno
|
||||
# self.key = f"{title}_{filename}_{line_number}"
|
||||
# if self.key not in self.storedSecs:
|
||||
# self.storedSecs[self.key] = {}
|
||||
|
||||
def __enter__(self):
|
||||
if self.enable is False:
|
||||
return
|
||||
self.start = time.time()
|
||||
return self
|
||||
# def __enter__(self):
|
||||
# if self.enable is False:
|
||||
# return
|
||||
# self.start = time.time()
|
||||
# return self
|
||||
|
||||
def __exit__(self, *_):
|
||||
if self.enable is False:
|
||||
return
|
||||
self.end = time.time()
|
||||
self.secs = self.end - self.start
|
||||
self.msecs = self.secs * 1000 # millisecs
|
||||
self.storedSecs[self.key].append(self.secs)
|
||||
self.storedSecs[self.key] = self.storedSecs[self.key][-self.maxStores :]
|
||||
self.avrSecs = sum(self.storedSecs[self.key]) / len(self.storedSecs[self.key])
|
||||
# def __exit__(self, *_):
|
||||
# if self.enable is False:
|
||||
# return
|
||||
# self.end = time.time()
|
||||
# self.secs = self.end - self.start
|
||||
# self.msecs = self.secs * 1000 # millisecs
|
||||
# self.storedSecs[self.key].append(self.secs)
|
||||
# self.storedSecs[self.key] = self.storedSecs[self.key][-self.maxStores :]
|
||||
# self.avrSecs = sum(self.storedSecs[self.key]) / len(self.storedSecs[self.key])
|
||||
|
||||
|
||||
class Timer2(object):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user