mirror of
https://github.com/coqui-ai/TTS.git
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6fdb88f8e2
* add configs * Update config file * Add model configs * Add model layers * Add layer files * Add layer modules * change config names * Add emotion manager * fIX missing ap bug * Fix missing ap bug * Add base TTS e2e class * Fix wrong variable name in load_tts_samples * Add training script * Remove range predictor and gaussian upsampling * Add helper function * Add vctk recipe * Add conformer docs * Fix linting in conformer.py * Add Docs * remove duplicate import * refactor args * Fix bugs * Removew emotion embedding * remove unused arg * Remove emotion embedding arg * Remove emotion embedding arg * fix style issues * Fix bugs * Fix bugs * Add unittests * make style * fix formatter bug * fix test * Add pyworld compute pitch func * Update requirments.txt * Fix dataset Bug * Chnge layer norm to instance norm * Add missing import * Remove emotions.py * remove ssim loss * Add init layers func to aligner * refactor model layers * remove audio_config arg * Rename loss func * Rename to delightful-tts * Rename loss func * Remove unused modules * refactor imports * replace audio config with audio processor * Add change sample rate option * remove broken resample func * update recipe * fix style, add config docs * fix tests and multispeaker embd dim * remove pyworld * Make style and fix inference * Split tts tests * Fixup * Fixup * Fixup * Add argument names * Set "random" speaker in the model Tortoise/Bark * Use a diff f0_cache path for delightfull tts * Fix delightful speaker handling * Fix lint * Make style --------- Co-authored-by: loganhart420 <loganartpersonal@gmail.com> Co-authored-by: Eren Gölge <erogol@hotmail.com>
148 lines
5.9 KiB
Python
148 lines
5.9 KiB
Python
import torch as T
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from TTS.tts.models.forward_tts import ForwardTTS, ForwardTTSArgs
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from TTS.tts.utils.helpers import sequence_mask
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# pylint: disable=unused-variable
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def expand_encoder_outputs_test():
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model = ForwardTTS(ForwardTTSArgs(num_chars=10))
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inputs = T.rand(2, 5, 57)
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durations = T.randint(1, 4, (2, 57))
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x_mask = T.ones(2, 1, 57)
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y_mask = T.ones(2, 1, durations.sum(1).max())
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expanded, _ = model.expand_encoder_outputs(inputs, durations, x_mask, y_mask)
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for b in range(durations.shape[0]):
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index = 0
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for idx, dur in enumerate(durations[b]):
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diff = (
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expanded[b, :, index : index + dur.item()]
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- inputs[b, :, idx].repeat(dur.item()).view(expanded[b, :, index : index + dur.item()].shape)
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).sum()
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assert abs(diff) < 1e-6, diff
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index += dur
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def model_input_output_test():
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"""Assert the output shapes of the model in different modes"""
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# VANILLA MODEL
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model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=False))
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x = T.randint(0, 10, (2, 21))
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x_lengths = T.randint(10, 22, (2,))
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x_lengths[-1] = 21
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x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
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durations = T.randint(1, 4, (2, 21))
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durations = durations * x_mask.squeeze(1)
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y_lengths = durations.sum(1)
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y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
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outputs = model.forward(x, x_lengths, y_lengths, dr=durations)
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assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
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assert outputs["durations_log"].shape == (2, 21)
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assert outputs["durations"].shape == (2, 21)
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assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
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assert (outputs["x_mask"] - x_mask).sum() == 0.0
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assert (outputs["y_mask"] - y_mask).sum() == 0.0
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assert outputs["alignment_soft"] is None
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assert outputs["alignment_mas"] is None
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assert outputs["alignment_logprob"] is None
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assert outputs["o_alignment_dur"] is None
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assert outputs["pitch_avg"] is None
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assert outputs["pitch_avg_gt"] is None
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# USE PITCH
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model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=False))
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x = T.randint(0, 10, (2, 21))
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x_lengths = T.randint(10, 22, (2,))
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x_lengths[-1] = 21
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x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
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durations = T.randint(1, 4, (2, 21))
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durations = durations * x_mask.squeeze(1)
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y_lengths = durations.sum(1)
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y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
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pitch = T.rand(2, 1, y_lengths.max())
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outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch)
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assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
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assert outputs["durations_log"].shape == (2, 21)
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assert outputs["durations"].shape == (2, 21)
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assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
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assert (outputs["x_mask"] - x_mask).sum() == 0.0
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assert (outputs["y_mask"] - y_mask).sum() == 0.0
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assert outputs["pitch_avg"].shape == (2, 1, 21)
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assert outputs["pitch_avg_gt"].shape == (2, 1, 21)
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assert outputs["alignment_soft"] is None
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assert outputs["alignment_mas"] is None
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assert outputs["alignment_logprob"] is None
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assert outputs["o_alignment_dur"] is None
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# USE ALIGNER NETWORK
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model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=True))
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x = T.randint(0, 10, (2, 21))
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x_lengths = T.randint(10, 22, (2,))
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x_lengths[-1] = 21
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x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
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durations = T.randint(1, 4, (2, 21))
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durations = durations * x_mask.squeeze(1)
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y_lengths = durations.sum(1)
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y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
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y = T.rand(2, y_lengths.max(), 80)
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outputs = model.forward(x, x_lengths, y_lengths, dr=durations, y=y)
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assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
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assert outputs["durations_log"].shape == (2, 21)
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assert outputs["durations"].shape == (2, 21)
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assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
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assert (outputs["x_mask"] - x_mask).sum() == 0.0
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assert (outputs["y_mask"] - y_mask).sum() == 0.0
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assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21)
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assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21)
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assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21)
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assert outputs["o_alignment_dur"].shape == (2, 21)
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assert outputs["pitch_avg"] is None
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assert outputs["pitch_avg_gt"] is None
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# USE ALIGNER NETWORK AND PITCH
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model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=True))
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x = T.randint(0, 10, (2, 21))
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x_lengths = T.randint(10, 22, (2,))
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x_lengths[-1] = 21
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x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
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durations = T.randint(1, 4, (2, 21))
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durations = durations * x_mask.squeeze(1)
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y_lengths = durations.sum(1)
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y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
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y = T.rand(2, y_lengths.max(), 80)
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pitch = T.rand(2, 1, y_lengths.max())
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outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch, y=y)
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assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
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assert outputs["durations_log"].shape == (2, 21)
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assert outputs["durations"].shape == (2, 21)
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assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
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assert (outputs["x_mask"] - x_mask).sum() == 0.0
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assert (outputs["y_mask"] - y_mask).sum() == 0.0
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assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21)
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assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21)
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assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21)
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assert outputs["o_alignment_dur"].shape == (2, 21)
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assert outputs["pitch_avg"].shape == (2, 1, 21)
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assert outputs["pitch_avg_gt"].shape == (2, 1, 21)
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