(self, samples)
| 68 | return sample |
| 69 | |
| 70 | def collater(self, samples): |
| 71 | if len(samples) == 0: |
| 72 | return {} |
| 73 | id = torch.LongTensor([s['id'] for s in samples]) |
| 74 | item_names = [s['item_name'] for s in samples] |
| 75 | text = [s['text'] for s in samples] |
| 76 | f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) |
| 77 | pitch = utils.collate_1d([s['pitch'] for s in samples]) |
| 78 | uv = utils.collate_1d([s['uv'] for s in samples]) |
| 79 | mels = utils.collate_2d([s['mel'] for s in samples], 0.0) |
| 80 | mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) |
| 81 | # mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \ |
| 82 | # if samples[0]['mel2ph'] is not None else None |
| 83 | # mel_nonpaddings = utils.collate_1d([s['mel_nonpadding'].float() for s in samples], 0.0) |
| 84 | |
| 85 | batch = { |
| 86 | 'id': id, |
| 87 | 'item_name': item_names, |
| 88 | 'nsamples': len(samples), |
| 89 | 'text': text, |
| 90 | 'mels': mels, |
| 91 | 'mel_lengths': mel_lengths, |
| 92 | 'pitch': pitch, |
| 93 | # 'mel2ph': mel2ph, |
| 94 | # 'mel_nonpaddings': mel_nonpaddings, |
| 95 | 'f0': f0, |
| 96 | 'uv': uv, |
| 97 | } |
| 98 | return batch |
| 99 | |
| 100 | |
| 101 | class PitchExtractionTask(FastSpeech2Task): |
nothing calls this directly
no outgoing calls
no test coverage detected