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Add slurp model files
Browse files- .DS_Store +0 -0
- README.md +3 -3
- app.py +118 -0
- audio--1504190171-headset.flac +0 -0
- packages.txt +2 -0
- requirements.txt +6 -0
- slurp/config.yaml +754 -0
- slurp/valid.acc.ave_10best.pth +3 -0
.DS_Store
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Binary file (10.2 kB). View file
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README.md
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@@ -1,7 +1,7 @@
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---
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title: ESPnet2
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emoji:
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colorFrom:
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colorTo: green
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sdk: gradio
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app_file: app.py
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---
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title: ESPnet2 TTS
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emoji: 📈
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colorFrom: green
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colorTo: green
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sdk: gradio
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app_file: app.py
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app.py
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import gradio as gr
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import time
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import torch
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import scipy.io.wavfile
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from espnet2.bin.tts_inference import Text2Speech
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from espnet2.utils.types import str_or_none
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from espnet2.bin.asr_inference import Speech2Text
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# tagen = 'kan-bayashi/ljspeech_vits'
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# vocoder_tagen = "none"
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speech2text = Speech2Text.from_pretrained(
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asr_train_config="slurp/config.yaml",
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asr_model_file="slurp/valid.acc.ave_10best.pth",
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# Decoding parameters are not included in the model file
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nbest=1
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)
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# Confirm the sampling rate is equal to that of the training corpus.
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# If not, you need to resample the audio data before inputting to speech2text
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speech, rate = soundfile.read("audio--1504190171-headset.flac")
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nbests = speech2text(speech)
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text, *_ = nbests[0]
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print(text)
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exit()
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text2speechen = Text2Speech.from_pretrained(
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model_tag=str_or_none(tagen),
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vocoder_tag=str_or_none(vocoder_tagen),
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device="cpu",
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# Only for Tacotron 2 & Transformer
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threshold=0.5,
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# Only for Tacotron 2
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minlenratio=0.0,
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maxlenratio=10.0,
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use_att_constraint=False,
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backward_window=1,
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forward_window=3,
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# Only for FastSpeech & FastSpeech2 & VITS
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speed_control_alpha=1.0,
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# Only for VITS
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noise_scale=0.333,
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noise_scale_dur=0.333,
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)
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tagjp = 'kan-bayashi/jsut_full_band_vits_prosody'
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vocoder_tagjp = 'none'
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text2speechjp = Text2Speech.from_pretrained(
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model_tag=str_or_none(tagjp),
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vocoder_tag=str_or_none(vocoder_tagjp),
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device="cpu",
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# Only for Tacotron 2 & Transformer
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threshold=0.5,
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# Only for Tacotron 2
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minlenratio=0.0,
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maxlenratio=10.0,
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use_att_constraint=False,
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backward_window=1,
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forward_window=3,
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# Only for FastSpeech & FastSpeech2 & VITS
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speed_control_alpha=1.0,
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# Only for VITS
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noise_scale=0.333,
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noise_scale_dur=0.333,
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)
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tagch = 'kan-bayashi/csmsc_full_band_vits'
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vocoder_tagch = "none"
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text2speechch = Text2Speech.from_pretrained(
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model_tag=str_or_none(tagch),
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vocoder_tag=str_or_none(vocoder_tagch),
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device="cpu",
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# Only for Tacotron 2 & Transformer
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threshold=0.5,
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# Only for Tacotron 2
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minlenratio=0.0,
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maxlenratio=10.0,
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use_att_constraint=False,
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backward_window=1,
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forward_window=3,
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# Only for FastSpeech & FastSpeech2 & VITS
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speed_control_alpha=1.0,
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# Only for VITS
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noise_scale=0.333,
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noise_scale_dur=0.333,
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)
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def inference(text,lang):
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with torch.no_grad():
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if lang == "english":
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wav = text2speechen(text)["wav"]
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scipy.io.wavfile.write("out.wav",text2speechen.fs , wav.view(-1).cpu().numpy())
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if lang == "chinese":
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wav = text2speechch(text)["wav"]
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scipy.io.wavfile.write("out.wav",text2speechch.fs , wav.view(-1).cpu().numpy())
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if lang == "japanese":
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wav = text2speechjp(text)["wav"]
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scipy.io.wavfile.write("out.wav",text2speechjp.fs , wav.view(-1).cpu().numpy())
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return "out.wav"
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title = "ESPnet2-TTS"
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description = "Gradio demo for ESPnet2-TTS: Extending the Edge of TTS Research. To use it, simply add your audio, or click one of the examples to load them. Read more at the links below."
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2110.07840' target='_blank'>ESPnet2-TTS: Extending the Edge of TTS Research</a> | <a href='https://github.com/espnet/espnet' target='_blank'>Github Repo</a></p>"
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examples=[['This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit. ESPnet2-TTS extends our earlier version, ESPnet-TTS, by adding many new features, including: on-the-fly flexible pre-processing, joint training with neural vocoders, and state-of-the-art TTS models with extensions like full-band E2E text-to-waveform modeling, which simplify the training pipeline and further enhance TTS performance. The unified design of our recipes enables users to quickly reproduce state-of-the-art E2E-TTS results',"english"],['レシピの統一された設計により、ユーザーは最先端のE2E-TTSの結果をすばやく再現できます。また、推論用の統合Pythonインターフェースで事前にトレーニングされたモデルを多数提供し、ユーザーがベースラインサンプルを生成してデモを構築するための迅速な手段を提供します。',"japanese"],['对英语和日语语料库的实验评估表明,我们提供的模型合成了与真实情况相当的话语,达到了最先进的水平',"chinese"]]
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gr.Interface(
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inference,
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[gr.inputs.Textbox(label="input text",lines=10),gr.inputs.Radio(choices=["english", "chinese", "japanese"], type="value", default="english", label="language")],
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gr.outputs.Audio(type="file", label="Output"),
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title=title,
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description=description,
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article=article,
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enable_queue=True,
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examples=examples
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).launch(debug=True)
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audio--1504190171-headset.flac
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Binary file (40.9 kB). View file
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packages.txt
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cmake
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libsndfile1
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requirements.txt
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espnet==0.10.3
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pyopenjtalk==0.1.5
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parallel_wavegan==0.5.3
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espnet_model_zoo
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scipy
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torch
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slurp/config.yaml
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|
1 |
+
config: conf/tuning/train_asr_conformer.yaml
|
2 |
+
print_config: false
|
3 |
+
log_level: INFO
|
4 |
+
dry_run: false
|
5 |
+
iterator_type: sequence
|
6 |
+
output_dir: exp/asr_train_asr_conformer_raw_en_word
|
7 |
+
ngpu: 1
|
8 |
+
seed: 0
|
9 |
+
num_workers: 1
|
10 |
+
num_att_plot: 3
|
11 |
+
dist_backend: nccl
|
12 |
+
dist_init_method: env://
|
13 |
+
dist_world_size: null
|
14 |
+
dist_rank: null
|
15 |
+
local_rank: 0
|
16 |
+
dist_master_addr: null
|
17 |
+
dist_master_port: null
|
18 |
+
dist_launcher: null
|
19 |
+
multiprocessing_distributed: false
|
20 |
+
unused_parameters: false
|
21 |
+
sharded_ddp: false
|
22 |
+
cudnn_enabled: true
|
23 |
+
cudnn_benchmark: false
|
24 |
+
cudnn_deterministic: true
|
25 |
+
collect_stats: false
|
26 |
+
write_collected_feats: false
|
27 |
+
max_epoch: 50
|
28 |
+
patience: null
|
29 |
+
val_scheduler_criterion:
|
30 |
+
- valid
|
31 |
+
- loss
|
32 |
+
early_stopping_criterion:
|
33 |
+
- valid
|
34 |
+
- loss
|
35 |
+
- min
|
36 |
+
best_model_criterion:
|
37 |
+
- - valid
|
38 |
+
- acc
|
39 |
+
- max
|
40 |
+
keep_nbest_models: 10
|
41 |
+
grad_clip: 5.0
|
42 |
+
grad_clip_type: 2.0
|
43 |
+
grad_noise: false
|
44 |
+
accum_grad: 1
|
45 |
+
no_forward_run: false
|
46 |
+
resume: true
|
47 |
+
train_dtype: float32
|
48 |
+
use_amp: false
|
49 |
+
log_interval: null
|
50 |
+
use_tensorboard: true
|
51 |
+
use_wandb: false
|
52 |
+
wandb_project: null
|
53 |
+
wandb_id: null
|
54 |
+
wandb_entity: null
|
55 |
+
wandb_name: null
|
56 |
+
wandb_model_log_interval: -1
|
57 |
+
detect_anomaly: false
|
58 |
+
pretrain_path: null
|
59 |
+
init_param: []
|
60 |
+
ignore_init_mismatch: false
|
61 |
+
freeze_param: []
|
62 |
+
num_iters_per_epoch: null
|
63 |
+
batch_size: 20
|
64 |
+
valid_batch_size: null
|
65 |
+
batch_bins: 1000000
|
66 |
+
valid_batch_bins: null
|
67 |
+
train_shape_file:
|
68 |
+
- exp/asr_stats_raw_en_word/train/speech_shape
|
69 |
+
- exp/asr_stats_raw_en_word/train/text_shape.word
|
70 |
+
valid_shape_file:
|
71 |
+
- exp/asr_stats_raw_en_word/valid/speech_shape
|
72 |
+
- exp/asr_stats_raw_en_word/valid/text_shape.word
|
73 |
+
batch_type: folded
|
74 |
+
valid_batch_type: null
|
75 |
+
fold_length:
|
76 |
+
- 80000
|
77 |
+
- 150
|
78 |
+
sort_in_batch: descending
|
79 |
+
sort_batch: descending
|
80 |
+
multiple_iterator: false
|
81 |
+
chunk_length: 500
|
82 |
+
chunk_shift_ratio: 0.5
|
83 |
+
num_cache_chunks: 1024
|
84 |
+
train_data_path_and_name_and_type:
|
85 |
+
- - dump/raw/train/wav.scp
|
86 |
+
- speech
|
87 |
+
- sound
|
88 |
+
- - dump/raw/train/text
|
89 |
+
- text
|
90 |
+
- text
|
91 |
+
valid_data_path_and_name_and_type:
|
92 |
+
- - dump/raw/devel/wav.scp
|
93 |
+
- speech
|
94 |
+
- sound
|
95 |
+
- - dump/raw/devel/text
|
96 |
+
- text
|
97 |
+
- text
|
98 |
+
allow_variable_data_keys: false
|
99 |
+
max_cache_size: 0.0
|
100 |
+
max_cache_fd: 32
|
101 |
+
valid_max_cache_size: null
|
102 |
+
optim: adam
|
103 |
+
optim_conf:
|
104 |
+
lr: 0.0002
|
105 |
+
scheduler: warmuplr
|
106 |
+
scheduler_conf:
|
107 |
+
warmup_steps: 25000
|
108 |
+
token_list:
|
109 |
+
- <blank>
|
110 |
+
- <unk>
|
111 |
+
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|
112 |
+
- s
|
113 |
+
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|
114 |
+
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|
115 |
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|
116 |
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|
117 |
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118 |
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|
119 |
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|
120 |
+
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|
121 |
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|
122 |
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- a
|
123 |
+
- ''''
|
124 |
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|
125 |
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|
126 |
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127 |
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|
128 |
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129 |
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130 |
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131 |
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132 |
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133 |
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134 |
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|
135 |
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|
136 |
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|
137 |
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|
138 |
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|
139 |
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140 |
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141 |
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- l
|
142 |
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|
143 |
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145 |
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|
146 |
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|
147 |
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148 |
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|
149 |
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|
150 |
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|
151 |
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|
152 |
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|
153 |
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|
154 |
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|
155 |
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|
156 |
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- b
|
157 |
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|
158 |
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159 |
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|
160 |
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|
161 |
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|
162 |
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- email_query
|
163 |
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|
164 |
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- ▁re
|
165 |
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|
166 |
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|
167 |
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|
168 |
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- at
|
169 |
+
- ▁have
|
170 |
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- g
|
171 |
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|
172 |
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|
173 |
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- en
|
174 |
+
- email_sendemail
|
175 |
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|
176 |
+
- 'on'
|
177 |
+
- ▁new
|
178 |
+
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|
179 |
+
- qa_factoid
|
180 |
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- calendar_set
|
181 |
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- ▁any
|
182 |
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- or
|
183 |
+
- ▁g
|
184 |
+
- ▁how
|
185 |
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|
186 |
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|
187 |
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- ch
|
188 |
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- ▁not
|
189 |
+
- ▁about
|
190 |
+
- ▁at
|
191 |
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|
192 |
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|
193 |
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|
194 |
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|
195 |
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|
196 |
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|
197 |
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|
198 |
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|
199 |
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- in
|
200 |
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- k
|
201 |
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- ic
|
202 |
+
- social_post
|
203 |
+
- ▁are
|
204 |
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|
205 |
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|
206 |
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|
207 |
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|
208 |
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|
209 |
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210 |
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|
211 |
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|
212 |
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|
213 |
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|
214 |
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|
215 |
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|
216 |
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|
217 |
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|
218 |
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|
219 |
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|
220 |
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|
221 |
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|
222 |
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|
223 |
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224 |
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|
225 |
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|
226 |
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|
227 |
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228 |
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|
229 |
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|
230 |
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|
231 |
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|
232 |
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|
233 |
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|
234 |
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235 |
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236 |
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237 |
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238 |
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239 |
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240 |
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241 |
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|
242 |
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243 |
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244 |
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|
245 |
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|
246 |
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|
247 |
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- x
|
248 |
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- general_repeat
|
249 |
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|
250 |
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- ou
|
251 |
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- ▁again
|
252 |
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|
253 |
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|
254 |
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|
255 |
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|
256 |
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|
257 |
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|
258 |
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|
259 |
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|
260 |
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|
261 |
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|
262 |
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263 |
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|
264 |
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265 |
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266 |
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|
267 |
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|
268 |
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|
269 |
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|
270 |
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271 |
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272 |
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273 |
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274 |
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275 |
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276 |
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284 |
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285 |
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287 |
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290 |
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291 |
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293 |
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294 |
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295 |
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296 |
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297 |
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298 |
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299 |
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300 |
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301 |
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302 |
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303 |
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304 |
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309 |
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312 |
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314 |
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316 |
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317 |
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318 |
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319 |
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320 |
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321 |
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322 |
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325 |
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326 |
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327 |
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328 |
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329 |
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330 |
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331 |
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332 |
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334 |
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335 |
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336 |
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338 |
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386 |
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|
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|
460 |
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|
461 |
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|
462 |
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|
463 |
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- iot_hue_lightoff
|
464 |
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|
465 |
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|
466 |
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|
467 |
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- ▁say
|
468 |
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|
469 |
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|
470 |
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|
471 |
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|
472 |
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|
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|
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|
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|
476 |
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|
477 |
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|
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|
479 |
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|
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|
481 |
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|
482 |
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|
483 |
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|
484 |
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|
485 |
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|
486 |
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|
487 |
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|
488 |
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|
489 |
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|
490 |
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|
491 |
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|
492 |
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|
493 |
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- ther
|
494 |
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- ▁thanks
|
495 |
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|
496 |
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|
497 |
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|
498 |
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|
499 |
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|
500 |
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|
501 |
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|
502 |
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|
503 |
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|
504 |
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|
505 |
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|
506 |
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|
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|
508 |
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|
509 |
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|
510 |
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|
511 |
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|
512 |
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|
513 |
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|
514 |
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|
515 |
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|
516 |
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|
517 |
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|
518 |
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|
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|
520 |
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|
521 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
533 |
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|
534 |
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|
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|
536 |
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|
537 |
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|
538 |
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|
539 |
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|
540 |
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|
541 |
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|
542 |
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|
543 |
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|
544 |
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|
545 |
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|
546 |
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- iot_hue_lightchange
|
547 |
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- pp
|
548 |
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- iot_coffee
|
549 |
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|
550 |
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|
551 |
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|
552 |
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|
553 |
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|
554 |
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|
555 |
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|
556 |
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|
557 |
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|
558 |
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|
559 |
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|
560 |
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|
561 |
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|
562 |
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|
563 |
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|
564 |
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|
565 |
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|
566 |
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|
567 |
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|
568 |
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|
569 |
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|
570 |
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|
571 |
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|
572 |
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|
573 |
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|
574 |
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|
575 |
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|
576 |
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|
577 |
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|
578 |
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|
579 |
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|
580 |
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|
581 |
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|
582 |
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|
583 |
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|
584 |
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|
585 |
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|
586 |
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|
587 |
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|
588 |
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|
589 |
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- audio_volume_down
|
590 |
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|
591 |
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|
592 |
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|
593 |
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|
594 |
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|
595 |
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|
596 |
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|
597 |
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|
598 |
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|
599 |
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|
600 |
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|
601 |
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|
602 |
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|
603 |
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|
604 |
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|
605 |
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|
606 |
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|
607 |
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|
608 |
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|
609 |
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|
610 |
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|
611 |
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|
612 |
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|
613 |
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|
614 |
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|
615 |
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|
616 |
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|
617 |
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|
618 |
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|
619 |
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|
620 |
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|
621 |
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|
622 |
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|
623 |
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|
624 |
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|
625 |
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|
626 |
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|
627 |
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|
628 |
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- '?'
|
629 |
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|
630 |
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|
631 |
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|
632 |
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|
633 |
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|
634 |
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|
635 |
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- ove
|
636 |
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|
637 |
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|
638 |
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|
639 |
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|
640 |
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|
641 |
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642 |
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|
643 |
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|
644 |
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|
645 |
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|
646 |
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|
647 |
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|
648 |
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|
649 |
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|
650 |
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|
651 |
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|
652 |
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|
653 |
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|
654 |
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|
655 |
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|
656 |
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|
657 |
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|
658 |
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|
659 |
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|
660 |
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|
661 |
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|
662 |
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- '0'
|
663 |
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|
664 |
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|
665 |
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|
666 |
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|
667 |
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- list
|
668 |
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|
669 |
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|
670 |
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- '7'
|
671 |
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|
672 |
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|
673 |
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- reci
|
674 |
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- ▁doll
|
675 |
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- <sos/eos>
|
676 |
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init: null
|
677 |
+
input_size: null
|
678 |
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ctc_conf:
|
679 |
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dropout_rate: 0.0
|
680 |
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ctc_type: builtin
|
681 |
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reduce: true
|
682 |
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ignore_nan_grad: true
|
683 |
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model_conf:
|
684 |
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ctc_weight: 0.3
|
685 |
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lsm_weight: 0.1
|
686 |
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length_normalized_loss: false
|
687 |
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extract_feats_in_collect_stats: false
|
688 |
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use_preprocessor: true
|
689 |
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token_type: word
|
690 |
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bpemodel: null
|
691 |
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non_linguistic_symbols: null
|
692 |
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cleaner: null
|
693 |
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g2p: null
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694 |
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speech_volume_normalize: null
|
695 |
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rir_scp: null
|
696 |
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rir_apply_prob: 1.0
|
697 |
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noise_scp: null
|
698 |
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noise_apply_prob: 1.0
|
699 |
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noise_db_range: '13_15'
|
700 |
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frontend: default
|
701 |
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frontend_conf:
|
702 |
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fs: 16k
|
703 |
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specaug: specaug
|
704 |
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specaug_conf:
|
705 |
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apply_time_warp: true
|
706 |
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time_warp_window: 5
|
707 |
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time_warp_mode: bicubic
|
708 |
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apply_freq_mask: true
|
709 |
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freq_mask_width_range:
|
710 |
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- 0
|
711 |
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- 30
|
712 |
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num_freq_mask: 2
|
713 |
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apply_time_mask: true
|
714 |
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time_mask_width_range:
|
715 |
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- 0
|
716 |
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- 40
|
717 |
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num_time_mask: 2
|
718 |
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normalize: utterance_mvn
|
719 |
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normalize_conf: {}
|
720 |
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preencoder: null
|
721 |
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preencoder_conf: {}
|
722 |
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encoder: conformer
|
723 |
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encoder_conf:
|
724 |
+
output_size: 512
|
725 |
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attention_heads: 8
|
726 |
+
linear_units: 2048
|
727 |
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num_blocks: 12
|
728 |
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dropout_rate: 0.1
|
729 |
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positional_dropout_rate: 0.1
|
730 |
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attention_dropout_rate: 0.1
|
731 |
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input_layer: conv2d
|
732 |
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normalize_before: true
|
733 |
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macaron_style: true
|
734 |
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pos_enc_layer_type: rel_pos
|
735 |
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selfattention_layer_type: rel_selfattn
|
736 |
+
activation_type: swish
|
737 |
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use_cnn_module: true
|
738 |
+
cnn_module_kernel: 31
|
739 |
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postencoder: null
|
740 |
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postencoder_conf: {}
|
741 |
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decoder: transformer
|
742 |
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decoder_conf:
|
743 |
+
attention_heads: 8
|
744 |
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linear_units: 2048
|
745 |
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num_blocks: 6
|
746 |
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dropout_rate: 0.1
|
747 |
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positional_dropout_rate: 0.1
|
748 |
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self_attention_dropout_rate: 0.1
|
749 |
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src_attention_dropout_rate: 0.1
|
750 |
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required:
|
751 |
+
- output_dir
|
752 |
+
- token_list
|
753 |
+
version: 0.10.3a2
|
754 |
+
distributed: false
|
slurp/valid.acc.ave_10best.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0b2144a9a26a71d820fd5923124e33cf8be4accf7e731e3c72526805350806d9
|
3 |
+
size 437699867
|