forked from mrq/tortoise-tts
fixed voicefixing not working as intended, load TTS before Gradio in the webui due to how long it takes to initialize tortoise (instead of just having a block to preload it)
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@ -132,6 +132,7 @@ I'll try and make a list of "common" (or what I feel may be common that I experi
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* `torch.cuda.OutOfMemoryError: CUDA out of memory.`: You most likely have a GPU with low VRAM (~4GiB), and the small optimizations with keeping data on the GPU is enough to OOM. Please open the `start.bat` file and add `--low-vram` to the command (for example: `py app.py --low-vram`) to disable those small optimizations.
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* `WavFileWarning: Chunk (non-data) not understood, skipping it.`: something about your WAVs are funny, and its best to remux your audio files with FFMPEG (included batch file in `.\convert\`).
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- Honestly, I don't know if this does impact output quality, as I feel it's placebo when I do try and correct this.
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* `Unable to find a valid cuDNN algorithm to run convolution`: a rather weird error message that occurs in the colab notebook. The vanilla auto-batch size calculation is a bit flawed, so try and reduce it to a fixed number in `Settings`, like eight or so.
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#### Non-"""Issues"""
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@ -42,8 +42,7 @@
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"%cd tortoise-tts\n",
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"!python -m pip install --upgrade pip\n",
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"!pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116\n",
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"!python -m pip install -r ./requirements.txt\n",
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"!python setup.py install"
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"!python -m pip install -r ./requirements.txt\n"
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]
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},
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{
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@ -78,9 +77,9 @@
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"sys.argv = [\"\"]\n",
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"\n",
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"mrq.args = mrq.setup_args()\n",
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"mrq.tts = mrq.setup_tortoise()\n",
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"mrq.webui = mrq.setup_gradio()\n",
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"mrq.webui.launch(share=True, prevent_thread_lock=True, height=1000)\n",
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"mrq.tts = mrq.setup_tortoise()\n",
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"mrq.webui.block_thread()"
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],
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"metadata":{
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8
webui.py
8
webui.py
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@ -190,7 +190,7 @@ def generate(
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'time': run_time
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}
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# save here in case some error happens mid-batch
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torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate)
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torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, tts.output_sample_rate)
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for k in audio_cache:
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audio = audio_cache[k]['audio']
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@ -265,10 +265,10 @@ def generate(
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if args.voice_fixer and voicefixer:
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# we could do this on the pieces before they get stiched up anyways to save some compute
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# but the stitching would need to read back from disk, defeating the point of caching the waveform
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for path in progress.tqdm(audio_cache, desc="Running voicefix..."):
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for path in progress.tqdm(output_voices, desc="Running voicefix..."):
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voicefixer.restore(
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input=f'{outdir}/{voice}_{k}.wav',
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output=f'{outdir}/{voice}_{k}.wav',
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input=path,
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output=path,
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#cuda=False,
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#mode=mode,
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)
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