minor readme fixes, citations
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README.md
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@@ -71,7 +71,7 @@ from datasets import load_dataset
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from speech_collator import SpeechCollator
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from torch.utils.data import DataLoader
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dataset = load_dataset('
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speaker2ixd = json.load(open("speaker2idx.json"))
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phone2ixd = json.load(open("phone2idx.json"))
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@@ -101,4 +101,37 @@ with open("speaker2idx.json", "w") as f:
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json.dump(speaker2idx, f)
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with open("phone2idx.json", "w") as f:
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json.dump(phone2idx, f)
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```
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from speech_collator import SpeechCollator
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from torch.utils.data import DataLoader
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dataset = load_dataset('cdminix/libritts-aligned', split="train")
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speaker2ixd = json.load(open("speaker2idx.json"))
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phone2ixd = json.load(open("phone2idx.json"))
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json.dump(speaker2idx, f)
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with open("phone2idx.json", "w") as f:
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json.dump(phone2idx, f)
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```
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### Measures
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When using ``speech-collator`` you can also use the ``measures`` argument to specify which measures to use. The following example extracts Pitch and Energy on the fly.
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```python
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import json
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from datasets import load_dataset
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from speech_collator import SpeechCollator
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from speech_collator.measures import PitchMeasure, EnergyMeasure
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from torch.utils.data import DataLoader
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dataset = load_dataset('cdminix/libritts-aligned', split="train")
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collator = SpeechCollator(
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speaker2ixd=speaker2idx,
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phone2ixd=phone2idx,
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measures=[PitchMeasure(), EnergyMeasure()],
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return_keys=["pitch", "energy"],
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)
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dataloader = DataLoader(dataset, collate_fn=collator.collate_fn, batch_size=8)
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```
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COMING SOON: Detailed documentation on how to use the measures at [MiniXC/speech-collator](https://www.github.com/MiniXC/speech-collator).
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# Citation
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When using LibriTTS please cite the following papers:
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- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://arxiv.org/abs/1904.02882)
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- [Montreal Forced Aligner: Trainable text-speech alignment using Kaldi](https://www.researchgate.net/publication/319185277_Montreal_Forced_Aligner_Trainable_Text-Speech_Alignment_Using_Kaldi)
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When using the Measures please cite the following paper (ours):
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- [Evaluating and reducing the distance between synthetic and real speech distributions](https://arxiv.org/abs/2211.16049)
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