mirror of https://github.com/coqui-ai/TTS.git
Merge pull request #519 from mueller91/dev
Speaker Encoder: New Datasets + DataLoader optimizedpull/10/head
Eren Gölge
GitHub
commit
c514628d02
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@ -42,8 +42,12 @@ def setup_loader(ap, is_val=False, verbose=False):
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dataset = MyDataset(ap,
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meta_data_eval if is_val else meta_data_train,
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voice_len=1.6,
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num_utter_per_speaker=10,
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num_utter_per_speaker=c.num_utters_per_speaker,
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num_speakers_in_batch=c.num_speakers_in_batch,
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skip_speakers=False,
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storage_size=c.storage["storage_size"],
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sample_from_storage_p=c.storage["sample_from_storage_p"],
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additive_noise=c.storage["additive_noise"],
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verbose=verbose)
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# sampler = DistributedSampler(dataset) if num_gpus > 1 else None
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loader = DataLoader(dataset,
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@ -60,6 +64,7 @@ def train(model, criterion, optimizer, scheduler, ap, global_step):
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epoch_time = 0
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best_loss = float('inf')
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avg_loss = 0
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avg_loader_time = 0
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end_time = time.time()
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for _, data in enumerate(data_loader):
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start_time = time.time()
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@ -93,8 +98,11 @@ def train(model, criterion, optimizer, scheduler, ap, global_step):
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step_time = time.time() - start_time
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epoch_time += step_time
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avg_loss = 0.01 * loss.item(
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) + 0.99 * avg_loss if avg_loss != 0 else loss.item()
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# Averaged Loss and Averaged Loader Time
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avg_loss = 0.01 * loss.item() \
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+ 0.99 * avg_loss if avg_loss != 0 else loss.item()
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avg_loader_time = 1/c.num_loader_workers * loader_time + \
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(c.num_loader_workers-1) / c.num_loader_workers * avg_loader_time if avg_loader_time != 0 else loader_time
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current_lr = optimizer.param_groups[0]['lr']
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if global_step % c.steps_plot_stats == 0:
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@ -103,7 +111,8 @@ def train(model, criterion, optimizer, scheduler, ap, global_step):
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"loss": avg_loss,
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"lr": current_lr,
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"grad_norm": grad_norm,
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"step_time": step_time
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"step_time": step_time,
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"avg_loader_time": avg_loader_time
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}
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tb_logger.tb_train_epoch_stats(global_step, train_stats)
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figures = {
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@ -116,9 +125,9 @@ def train(model, criterion, optimizer, scheduler, ap, global_step):
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if global_step % c.print_step == 0:
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print(
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" | > Step:{} Loss:{:.5f} AvgLoss:{:.5f} GradNorm:{:.5f} "
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"StepTime:{:.2f} LoaderTime:{:.2f} LR:{:.6f}".format(
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"StepTime:{:.2f} LoaderTime:{:.2f} AvGLoaderTime:{:.2f} LR:{:.6f}".format(
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global_step, loss.item(), avg_loss, grad_norm, step_time,
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loader_time, current_lr),
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loader_time, avg_loader_time, current_lr),
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flush=True)
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# save best model
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@ -1,6 +1,6 @@
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{
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"run_name": "Model compatible to CorentinJ/Real-Time-Voice-Cloning",
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"run_name": "mueller91",
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"run_description": "train speaker encoder with voxceleb1, voxceleb2 and libriSpeech ",
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"audio":{
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// Audio processing parameters
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@ -23,11 +23,11 @@
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"clip_norm": true, // clip normalized values into the range.
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"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"do_trim_silence": true, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
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},
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"reinit_layers": [],
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"loss": "ge2e", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
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"loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
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"grad_clip": 3.0, // upper limit for gradients for clipping.
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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@ -35,27 +35,69 @@
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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"steps_plot_stats": 10, // number of steps to plot embeddings.
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"num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_speakers_in_batch": 64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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"num_utters_per_speaker": 10, //
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"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"wd": 0.000001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 1, // Number of steps to log traning on console.
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"output_path": "../../checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"print_step": 20, // Number of steps to log traning on console.
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"output_path": "../../MozillaTTSOutput/checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"model": {
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"input_dim": 40,
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"proj_dim": 256,
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"lstm_dim": 256,
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"lstm_dim": 768,
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"num_lstm_layers": 3,
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"use_lstm_with_projection": false
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"use_lstm_with_projection": true
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},
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"storage": {
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"sample_from_storage_p": 0.66, // the probability with which we'll sample from the DataSet in-memory storage
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"storage_size": 15, // the size of the in-memory storage with respect to a single batch
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"additive_noise": 1e-5 // add very small gaussian noise to the data in order to increase robustness
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},
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"datasets":
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[
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{
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"name": "vctk",
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"path": "../../../datasets/VCTK-Corpus-removed-silence/",
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"name": "vctk_slim",
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"path": "../../../audio-datasets/en/VCTK-Corpus/",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "libri_tts",
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"path": "../../../audio-datasets/en/LibriTTS/train-clean-100",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "libri_tts",
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"path": "../../../audio-datasets/en/LibriTTS/train-clean-360",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "libri_tts",
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"path": "../../../audio-datasets/en/LibriTTS/train-other-500",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "voxceleb1",
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"path": "../../../audio-datasets/en/voxceleb1/",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "voxceleb2",
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"path": "../../../audio-datasets/en/voxceleb2/",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "common_voice",
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"path": "../../../audio-datasets/en/MozillaCommonVoice",
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"meta_file_train": "train.tsv",
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"meta_file_val": "test.tsv"
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}
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]
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}
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@ -1,11 +1,15 @@
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import numpy
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import numpy as np
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import queue
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import torch
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import random
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from torch.utils.data import Dataset
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from tqdm import tqdm
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class MyDataset(Dataset):
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def __init__(self, ap, meta_data, voice_len=1.6, num_speakers_in_batch=64,
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storage_size=1, sample_from_storage_p=0.5, additive_noise=0,
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num_utter_per_speaker=10, skip_speakers=False, verbose=False):
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"""
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Args:
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@ -24,8 +28,15 @@ class MyDataset(Dataset):
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self.ap = ap
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self.verbose = verbose
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self.__parse_items()
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self.storage = queue.Queue(maxsize=storage_size*num_speakers_in_batch)
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self.sample_from_storage_p = float(sample_from_storage_p)
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self.additive_noise = float(additive_noise)
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if self.verbose:
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print("\n > DataLoader initialization")
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print(f" | > Speakers per Batch: {num_speakers_in_batch}")
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print(f" | > Storage Size: {self.storage.maxsize} speakers, each with {num_utter_per_speaker} utters")
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print(f" | > Sample_from_storage_p : {self.sample_from_storage_p}")
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print(f" | > Noise added : {self.additive_noise}")
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print(f" | > Number of instances : {len(self.items)}")
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print(f" | > Sequence length: {self.seq_len}")
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print(f" | > Num speakers: {len(self.speakers)}")
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@ -51,21 +62,37 @@ class MyDataset(Dataset):
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return sample
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def __parse_items(self):
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"""
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Find unique speaker ids and create a dict mapping utterances from speaker id
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"""
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speakers = list({item[-1] for item in self.items})
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self.speaker_to_utters = {}
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self.speakers = []
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for speaker in speakers:
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speaker_utters = [item[1] for item in self.items if item[2] == speaker]
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if len(speaker_utters) < self.num_utter_per_speaker and self.skip_speakers:
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print(
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f" [!] Skipped speaker {speaker}. Not enough utterances {self.num_utter_per_speaker} vs {len(speaker_utters)}."
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)
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for i in self.items:
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path_ = i[1]
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speaker_ = i[2]
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if speaker_ in self.speaker_to_utters.keys():
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self.speaker_to_utters[speaker_].append(path_)
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else:
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self.speakers.append(speaker)
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self.speaker_to_utters[speaker] = speaker_utters
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self.speaker_to_utters[speaker_] = [path_, ]
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if self.skip_speakers:
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self.speaker_to_utters = {k: v for (k, v) in self.speaker_to_utters.items() if
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len(v) >= self.num_utter_per_speaker}
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self.speakers = [k for (k, v) in self.speaker_to_utters.items()]
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# def __parse_items(self):
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# """
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# Find unique speaker ids and create a dict mapping utterances from speaker id
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# """
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# speakers = list({item[-1] for item in self.items})
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# self.speaker_to_utters = {}
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# self.speakers = []
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# for speaker in speakers:
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# speaker_utters = [item[1] for item in self.items if item[2] == speaker]
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# if len(speaker_utters) < self.num_utter_per_speaker and self.skip_speakers:
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# print(
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# f" [!] Skipped speaker {speaker}. Not enough utterances {self.num_utter_per_speaker} vs {len(speaker_utters)}."
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# )
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# else:
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# self.speakers.append(speaker)
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# self.speaker_to_utters[speaker] = speaker_utters
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def __len__(self):
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return int(1e10)
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@ -86,7 +113,7 @@ class MyDataset(Dataset):
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"""
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Sample all M utterances for the given speaker.
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"""
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feats = []
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wavs = []
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labels = []
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for _ in range(self.num_utter_per_speaker):
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# TODO:dummy but works
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@ -102,11 +129,9 @@ class MyDataset(Dataset):
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break
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self.speaker_to_utters[speaker].remove(utter)
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offset = random.randint(0, wav.shape[0] - self.seq_len)
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mel = self.ap.melspectrogram(wav[offset : offset + self.seq_len])
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feats.append(torch.FloatTensor(mel))
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wavs.append(wav)
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labels.append(speaker)
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return feats, labels
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return wavs, labels
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def __getitem__(self, idx):
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speaker, _ = self.__sample_speaker()
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@ -116,7 +141,28 @@ class MyDataset(Dataset):
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labels = []
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feats = []
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for speaker in batch:
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feats_, labels_ = self.__sample_speaker_utterances(speaker)
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if random.random() < self.sample_from_storage_p and self.storage.full():
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# sample from storage (if full), ignoring the speaker
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wavs_, labels_ = random.choice(self.storage.queue)
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else:
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# don't sample from storage, but from HDD
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wavs_, labels_ = self.__sample_speaker_utterances(speaker)
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# if storage is full, remove an item
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if self.storage.full():
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_ = self.storage.get_nowait()
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# put the newly loaded item into storage
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self.storage.put_nowait((wavs_, labels_))
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# add random gaussian noise
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if self.additive_noise > 0:
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noises_ = [numpy.random.normal(0, self.additive_noise, size=len(w)) for w in wavs_]
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wavs_ = [wavs_[i] + noises_[i] for i in range(len(wavs_))]
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# get a random subset of each of the wavs and convert to MFCC.
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offsets_ = [random.randint(0, wav.shape[0] - self.seq_len) for wav in wavs_]
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mels_ = [self.ap.melspectrogram(wavs_[i][offsets_[i]: offsets_[i] + self.seq_len]) for i in range(len(wavs_))]
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feats_ = [torch.FloatTensor(mel) for mel in mels_]
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labels.append(labels_)
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feats.extend(feats_)
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feats = torch.stack(feats)
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@ -23,7 +23,7 @@ def save_checkpoint(model, optimizer, model_loss, out_path,
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def save_best_model(model, optimizer, model_loss, best_loss, out_path,
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current_step):
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if model_loss < best_loss:
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if model_loss < best_loss and current_step > 1000:
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new_state_dict = model.state_dict()
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state = {
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'model': new_state_dict,
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@ -35,7 +35,7 @@ def save_best_model(model, optimizer, model_loss, best_loss, out_path,
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best_loss = model_loss
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bestmodel_path = 'best_model.pth.tar'
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bestmodel_path = os.path.join(out_path, bestmodel_path)
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print("\n > BEST MODEL ({0:.5f}) : {1:}".format(
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model_loss, bestmodel_path))
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print("\n > NEW BEST MODEL ({0:.5f}) : {1:}".format(
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model_loss, os.path.abspath(bestmodel_path)))
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torch.save(state, bestmodel_path)
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return best_loss
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@ -2,6 +2,10 @@ import os
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from glob import glob
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import re
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import sys
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from pathlib import Path
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from tqdm import tqdm
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from TTS.tts.utils.generic_utils import split_dataset
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@ -14,8 +18,8 @@ def load_meta_data(datasets):
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meta_file_train = dataset['meta_file_train']
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meta_file_val = dataset['meta_file_val']
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preprocessor = get_preprocessor_by_name(name)
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meta_data_train = preprocessor(root_path, meta_file_train)
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print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
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if meta_file_val is None:
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meta_data_eval, meta_data_train = split_dataset(meta_data_train)
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else:
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@ -167,8 +171,8 @@ def common_voice(root_path, meta_file):
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cols = line.split("\t")
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text = cols[2]
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speaker_name = cols[0]
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wav_file = os.path.join(root_path, "clips", cols[1] + ".wav")
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items.append([text, wav_file, speaker_name])
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wav_file = os.path.join(root_path, "clips", cols[1].replace(".mp3", ".wav"))
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items.append([text, wav_file, 'MCV_' + speaker_name])
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return items
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@ -187,7 +191,7 @@ def libri_tts(root_path, meta_files=None):
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cols = line.split('\t')
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wav_file = os.path.join(_root_path, cols[0] + '.wav')
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text = cols[1]
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items.append([text, wav_file, speaker_name])
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items.append([text, wav_file, 'LTTS_' + speaker_name])
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for item in items:
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assert os.path.exists(
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item[1]), f" [!] wav files don't exist - {item[1]}"
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@ -235,8 +239,7 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
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"""homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
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test_speakers = meta_files
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items = []
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meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt",
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recursive=True)
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meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
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for meta_file in meta_files:
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_, speaker_id, txt_file = os.path.relpath(meta_file,
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root_path).split(os.sep)
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|
@ -249,6 +252,70 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
|
|||
text = file_text.readlines()[0]
|
||||
wav_file = os.path.join(root_path, wavs_path, speaker_id,
|
||||
file_id + '.wav')
|
||||
items.append([text, wav_file, speaker_id])
|
||||
items.append([text, wav_file, 'VCTK_' + speaker_id])
|
||||
|
||||
return items
|
||||
|
||||
|
||||
def vctk_slim(root_path, meta_files=None, wavs_path='wav48'):
|
||||
"""homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
|
||||
items = []
|
||||
txt_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
|
||||
for text_file in txt_files:
|
||||
_, speaker_id, txt_file = os.path.relpath(text_file,
|
||||
root_path).split(os.sep)
|
||||
file_id = txt_file.split('.')[0]
|
||||
if isinstance(meta_files, list): # if is list ignore this speakers ids
|
||||
if speaker_id in meta_files:
|
||||
continue
|
||||
wav_file = os.path.join(root_path, wavs_path, speaker_id,
|
||||
file_id + '.wav')
|
||||
items.append([None, wav_file, 'VCTK_' + speaker_id])
|
||||
|
||||
return items
|
||||
|
||||
# ======================================== VOX CELEB ===========================================
|
||||
def voxceleb2(root_path, meta_file=None):
|
||||
"""
|
||||
:param meta_file Used only for consistency with load_meta_data api
|
||||
"""
|
||||
return _voxcel_x(root_path, meta_file, voxcel_idx="2")
|
||||
|
||||
|
||||
def voxceleb1(root_path, meta_file=None):
|
||||
"""
|
||||
:param meta_file Used only for consistency with load_meta_data api
|
||||
"""
|
||||
return _voxcel_x(root_path, meta_file, voxcel_idx="1")
|
||||
|
||||
|
||||
def _voxcel_x(root_path, meta_file, voxcel_idx):
|
||||
assert voxcel_idx in ["1", "2"]
|
||||
expected_count = 148_000 if voxcel_idx == "1" else 1_000_000
|
||||
voxceleb_path = Path(root_path)
|
||||
cache_to = voxceleb_path / f"metafile_voxceleb{voxcel_idx}.csv"
|
||||
cache_to.parent.mkdir(exist_ok=True)
|
||||
|
||||
# if not exists meta file, crawl recursively for 'wav' files
|
||||
if meta_file is not None:
|
||||
with open(str(meta_file), 'r') as f:
|
||||
return [x.strip().split('|') for x in f.readlines()]
|
||||
|
||||
elif not cache_to.exists():
|
||||
cnt = 0
|
||||
meta_data = ""
|
||||
wav_files = voxceleb_path.rglob("**/*.wav")
|
||||
for path in tqdm(wav_files, desc=f"Building VoxCeleb {voxcel_idx} Meta file ... this needs to be done only once.",
|
||||
total=expected_count):
|
||||
speaker_id = str(Path(path).parent.parent.stem)
|
||||
assert speaker_id.startswith('id')
|
||||
text = None # VoxCel does not provide transciptions, and they are not needed for training the SE
|
||||
meta_data += f"{text}|{path}|voxcel{voxcel_idx}_{speaker_id}\n"
|
||||
cnt += 1
|
||||
with open(str(cache_to), 'w') as f:
|
||||
f.write(meta_data)
|
||||
if cnt < expected_count:
|
||||
raise ValueError(f"Found too few instances for Voxceleb. Should be around {expected_count}, is: {cnt}")
|
||||
|
||||
with open(str(cache_to), 'r') as f:
|
||||
return [x.strip().split('|') for x in f.readlines()]
|
||||
|
|
|
|||
|
|
@ -7,11 +7,9 @@ from TTS.utils.generic_utils import check_argument
|
|||
|
||||
|
||||
def split_dataset(items):
|
||||
is_multi_speaker = False
|
||||
speakers = [item[-1] for item in items]
|
||||
is_multi_speaker = len(set(speakers)) > 1
|
||||
eval_split_size = 500 if len(items) * 0.01 > 500 else int(
|
||||
len(items) * 0.01)
|
||||
eval_split_size = min(500, int(len(items) * 0.01))
|
||||
assert eval_split_size > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
|
||||
np.random.seed(0)
|
||||
np.random.shuffle(items)
|
||||
|
|
@ -142,6 +140,11 @@ def check_config(c):
|
|||
check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
|
||||
check_argument('trim_db', c['audio'], restricted=True, val_type=int)
|
||||
|
||||
# storage parameters
|
||||
check_argument('sample_from_storage_p', c['storage'], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
|
||||
check_argument('storage_size', c['storage'], restricted=True, val_type=int, min_val=1, max_val=100)
|
||||
check_argument('additive_noise', c['storage'], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
|
||||
|
||||
# training parameters
|
||||
check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
|
||||
check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@ def save_best_model(target_loss, best_loss, model, optimizer, current_step, epoc
|
|||
if target_loss < best_loss:
|
||||
file_name = 'best_model.pth.tar'
|
||||
checkpoint_path = os.path.join(output_folder, file_name)
|
||||
print(" > BEST MODEL : {}".format(checkpoint_path))
|
||||
print(" >> BEST MODEL : {}".format(checkpoint_path))
|
||||
save_model(model, optimizer, current_step, epoch, r, checkpoint_path, model_loss=target_loss, **kwargs)
|
||||
best_loss = target_loss
|
||||
return best_loss
|
||||
|
|
|
|||
|
|
@ -1,10 +1,6 @@
|
|||
client_id path sentence up_votes down_votes age gender accent
|
||||
aa7af576605fee2c78c26b85497c64cb9c9fd97228071f8666d9f49f15bce01899bbb930fa60b76d212091d779d83b92e0b54c73cbb21d2c7e1eedc817e41cb3 21fce545b24d9a5af0403b949e95e8dd3c10c4ff3e371f14e4d5b4ebf588670b7c9e618285fc872d94a89ed7f0217d9019fe5de33f1577b49dcd518eacf63c4b Man sollte den Länderfinanzausgleich durch einen Bundesliga-Soli ersetzen. 2 0 fourties male germany
|
||||
aa7af576605fee2c78c26b85497c64cb9c9fd97228071f8666d9f49f15bce01899bbb930fa60b76d212091d779d83b92e0b54c73cbb21d2c7e1eedc817e41cb3 42758baa4e91ef6b82b78b11a04bc5117a035a8d3bc42c33c0bb3084909af17043a194cfd8cd9839f0d6ef1ea5413acda5de5d1936abcc8ca073e2da7f9488ea Folgende Lektüre kann ich Ihnen zum Thema Kognitionspsychologie empfehlen. 2 0 fourties male germany
|
||||
aa7af576605fee2c78c26b85497c64cb9c9fd97228071f8666d9f49f15bce01899bbb930fa60b76d212091d779d83b92e0b54c73cbb21d2c7e1eedc817e41cb3 478f172c2dbda6675247e9674ade79a5b49efeefb7c9e99040dcc69a847a01d69398cf180570859b0cdb6fc887717e04cd8b149c723d48d00b5d18f41314667c Touristen winkten den Leuten am Ufer zu. 2 0 fourties male germany
|
||||
aa7af576605fee2c78c26b85497c64cb9c9fd97228071f8666d9f49f15bce01899bbb930fa60b76d212091d779d83b92e0b54c73cbb21d2c7e1eedc817e41cb3 4854368d6d21cb44103e432b5332f31e8d14030582a40850501bcf9377d699314a5ff27a8206fa89254ddde7f3f1c65d33836f3dfcfa16bcabec08537f2b5f08 Valentin hat das Handtuch geworfen. 2 0 fourties male germany
|
||||
aa7af576605fee2c78c26b85497c64cb9c9fd97228071f8666d9f49f15bce01899bbb930fa60b76d212091d779d83b92e0b54c73cbb21d2c7e1eedc817e41cb3 a841a9f3e032495dd47560e65fba99eeacb3618c07de8b1351c20188e5b71e33cc52f73315f721a3a24b65763c65bb52fbf3ae052eb5774e834dcb57f296db5c Ohne Gehörschutz bei der Arbeit wäre Klaus wohl nach zwei Wochen taub. 2 0 fourties male germany
|
||||
aa7af576605fee2c78c26b85497c64cb9c9fd97228071f8666d9f49f15bce01899bbb930fa60b76d212091d779d83b92e0b54c73cbb21d2c7e1eedc817e41cb3 03ab970a5bf5410bc3260b073cce1c7f49c688ace83dc8836b1c0f79a09fea45a27725c769f4a9d2e6181defd016d22642789d7ac51da252b42958a9192bd4c7 Gerrit erinnerte sich daran, dass er einst einen Eid geschworen hatte. 2 0 fourties male germany
|
||||
aa7af576605fee2c78c26b85497c64cb9c9fd97228071f8666d9f49f15bce01899bbb930fa60b76d212091d779d83b92e0b54c73cbb21d2c7e1eedc817e41cb3 c4a94df443ad5f2c7241413ef7145d5f0de41ae929759073917fe96166da3c7d3a612c920ed7b0f3d5950a38d6205e9dba24af5bfb27e390a220d004e6e26744 Auf das, was jetzt kommt, habe ich nämlich absolut keinen Bock. 2 0 fourties male germany
|
||||
aa7af576605fee2c78c26b85497c64cb9c9fd97228071f8666d9f49f15bce01899bbb930fa60b76d212091d779d83b92e0b54c73cbb21d2c7e1eedc817e41cb3 104695983b1112229b4a48696405d044dad9ddef713aa6eb1a6240cc16b7b7a2a96354ae9da99783850dde08a982091e48d3037288a3a58269cac9fe70a6bd7a Von Salzburg ist es doch nicht weit bis zum Chiemsee. 2 0 fourties male germany
|
||||
d5b5da343bb0f65e3580bc2e1902a4f5d004241488d751503f2020bc1c93f89715e355e35f6e25def2b90cb3eea99fda403eb92ae3afbb84d039a54a4ed2d875 ad2f69e053b0e20e01c82b9821fe5787f1cc8e4b0b97f0e4cab1e9a652c577169c8244fb222281a60ee3081854014113e04c4ca43643100b7c01dab0fac11974 Warum werden da keine strafrechtlichen Konsequenzen gezogen? 2 0 thirties male germany
|
||||
client_id path sentence up_votes down_votes age gender accent locale segment
|
||||
95324d489b122a800b840e0b0d068f7363a1a6c2cd2e7365672cc7033e38deaa794bd59edcf8196aa35c9791652b9085ac3839a98bb50ebab4a1e8538a94846b common_voice_en_20005954.mp3 The applicants are invited for coffee and visa is given immediately. 3 0 en
|
||||
95324d489b122a800b840e0b0d068f7363a1a6c2cd2e7365672cc7033e38deaa794bd59edcf8196aa35c9791652b9085ac3839a98bb50ebab4a1e8538a94846b common_voice_en_20005955.mp3 Developmental robotics is related to, but differs from, evolutionary robotics. 2 0 en
|
||||
95324d489b122a800b840e0b0d068f7363a1a6c2cd2e7365672cc7033e38deaa794bd59edcf8196aa35c9791652b9085ac3839a98bb50ebab4a1e8538a94846b common_voice_en_20005956.mp3 The musical was originally directed and choreographed by Alan Lund. 2 0 en
|
||||
954a4181ae9fba89d1b1570f2ae148b3ee18ee2311de978e698f598db859f830d93d35574596d713518e8c96cdae01fce7a08c60c2e0a22bcf01e020924440a6 common_voice_en_19737073.mp3 He graduated from Columbia High School, in Brown County, South Dakota. 2 0 en
|
||||
954a4181ae9fba89d1b1570f2ae148b3ee18ee2311de978e698f598db859f830d93d35574596d713518e8c96cdae01fce7a08c60c2e0a22bcf01e020924440a6 common_voice_en_19737074.mp3 Competition for limited resources has also resulted in some local conflicts. 2 0 en
|
||||
|
|
|
|||
|
|
|
@ -11,18 +11,8 @@ class TestPreprocessors(unittest.TestCase):
|
|||
root_path = get_tests_input_path()
|
||||
meta_file = "common_voice.tsv"
|
||||
items = common_voice(root_path, meta_file)
|
||||
assert items[0][0] == "Man sollte den Länderfinanzausgleich durch " \
|
||||
"einen Bundesliga-Soli ersetzen."
|
||||
assert items[0][1] == os.path.join(get_tests_input_path(), "clips",
|
||||
"21fce545b24d9a5af0403b949e95e8dd3"
|
||||
"c10c4ff3e371f14e4d5b4ebf588670b7c"
|
||||
"9e618285fc872d94a89ed7f0217d9019f"
|
||||
"e5de33f1577b49dcd518eacf63c4b.wav")
|
||||
assert items[0][0] == 'The applicants are invited for coffee and visa is given immediately.'
|
||||
assert items[0][1] == os.path.join(get_tests_input_path(), "clips", "common_voice_en_20005954.wav")
|
||||
|
||||
assert items[-1][0] == "Warum werden da keine strafrechtlichen " \
|
||||
"Konsequenzen gezogen?"
|
||||
assert items[-1][1] == os.path.join(get_tests_input_path(), "clips",
|
||||
"ad2f69e053b0e20e01c82b9821fe5787f1"
|
||||
"cc8e4b0b97f0e4cab1e9a652c577169c82"
|
||||
"44fb222281a60ee3081854014113e04c4c"
|
||||
"a43643100b7c01dab0fac11974.wav")
|
||||
assert items[-1][0] == "Competition for limited resources has also resulted in some local conflicts."
|
||||
assert items[-1][1] == os.path.join(get_tests_input_path(), "clips", "common_voice_en_19737074.wav")
|
||||
|
|
|
|||
Loading…
Reference in New Issue