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Running
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Running
on
Zero
Upload app.py
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app.py
CHANGED
@@ -1,3 +1,4 @@
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from huggingface_hub import snapshot_download
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from katsu import Katsu
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from models import build_model
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@@ -245,6 +246,7 @@ def generate(text, voice='af', ps=None, speed=1, trim=3000, use_gpu='auto'):
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tokens = tokens[:510]
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ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
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use_gpu = len(ps) > 99 if use_gpu == 'auto' else use_gpu
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try:
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if use_gpu:
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out = forward_gpu(tokens, voices, speed)
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@@ -417,16 +419,18 @@ def lf_generate(segments, voice, speed=1, trim=0, pad_between=0, use_gpu=True):
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i = 0
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while i < len(token_lists):
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bs = batch_sizes.pop() if batch_sizes else 100
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try:
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if use_gpu:
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outs = lf_forward_gpu(
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else:
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outs = lf_forward(
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except gr.exceptions.Error as e:
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if use_gpu:
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gr.Warning(str(e))
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gr.Info('Switching to CPU')
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outs = lf_forward(
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use_gpu = False
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else:
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raise gr.Error(e)
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@@ -494,7 +498,7 @@ with gr.Blocks() as lf_tts:
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with gr.Blocks() as about:
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gr.Markdown('''
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Kokoro is a frontier TTS model for its size. It has [80 million](https://hf.co/spaces/hexgrad/Kokoro-TTS/blob/main/app.py#
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### FAQ
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**Will this be open sourced?**<br/>
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from datetime import datetime
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from huggingface_hub import snapshot_download
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from katsu import Katsu
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from models import build_model
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tokens = tokens[:510]
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ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
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use_gpu = len(ps) > 99 if use_gpu == 'auto' else use_gpu
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print('🔥', datetime.now(), len(ps), voices, use_gpu)
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try:
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if use_gpu:
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out = forward_gpu(tokens, voices, speed)
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i = 0
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while i < len(token_lists):
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bs = batch_sizes.pop() if batch_sizes else 100
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tokens = token_lists[i:i+bs]
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print('📖', datetime.now(), len(tokens), voices, use_gpu)
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try:
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if use_gpu:
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outs = lf_forward_gpu(tokens, voices, speed)
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else:
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outs = lf_forward(tokens, voices, speed)
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except gr.exceptions.Error as e:
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if use_gpu:
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gr.Warning(str(e))
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gr.Info('Switching to CPU')
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outs = lf_forward(tokens, voices, speed)
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use_gpu = False
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else:
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raise gr.Error(e)
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with gr.Blocks() as about:
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gr.Markdown('''
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Kokoro is a frontier TTS model for its size. It has [80 million](https://hf.co/spaces/hexgrad/Kokoro-TTS/blob/main/app.py#L32) parameters, uses a lean [StyleTTS 2](https://github.com/yl4579/StyleTTS2) architecture, and was trained on high-quality data. The weights are currently private, but a free public demo is hosted here, at `https://hf.co/spaces/hexgrad/Kokoro-TTS`. The Community tab is open for feature requests, bug reports, etc. For other inquiries, contact `@rzvzn` on Discord.
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### FAQ
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**Will this be open sourced?**<br/>
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