```
config: conf/tuning/transformer_fisherlike_4gpu_bbins16m_fix.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/st_transformer_fisherlike_4gpu_bbins16m_fix_raw_bpe_tc1000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 36641
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 3
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/st_stats_raw_bpe1000_sp/train/speech_shape
- exp/st_stats_raw_bpe1000_sp/train/text_shape.bpe
- exp/st_stats_raw_bpe1000_sp/train/src_text_shape.bpe
valid_shape_file:
- exp/st_stats_raw_bpe1000_sp/valid/speech_shape
- exp/st_stats_raw_bpe1000_sp/valid/text_shape.bpe
- exp/st_stats_raw_bpe1000_sp/valid/src_text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - /scratch/iwslt22dump//raw/train_sp/wav.scp
    - speech
    - kaldi_ark
-   - /scratch/iwslt22dump//raw/train_sp/text.tc.en
    - text
    - text
-   - /scratch/iwslt22dump//raw/train_sp/text.tc.rm.ta
    - src_text
    - text
valid_data_path_and_name_and_type:
-   - /scratch/iwslt22dump//raw/dev/wav.scp
    - speech
    - kaldi_ark
-   - /scratch/iwslt22dump//raw/dev/text.tc.en
    - text
    - text
-   - /scratch/iwslt22dump//raw/dev/text.tc.rm.ta
    - src_text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 12.5
scheduler: noamlr
scheduler_conf:
    model_size: 256
    warmup_steps: 25000
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- ▁sister
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- ut
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- j
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- ting
- ▁until
- ▁honestly
- ▁whole
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- ▁course
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- he
- up
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- ▁cha
- ▁clothes
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- ▁nice
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- ▁real
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- ▁sea
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- ▁summer
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- wi
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- ble
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- ▁picture
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- ight
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- ▁woman
- ▁otherwise
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- ▁luck
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- ▁paper
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- man
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- ade
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- ious
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- ▁kilo
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- ▁birthday
- ▁exhaust
- ▁point
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- ▁patience
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- ▁trouble
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- ▁weekend
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- ▁repeat
- ▁auntie
- ▁sign
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- ▁mahdi
- ▁sfax
- ▁annoy
- ▁dishes
- ▁message
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- ▁office
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- ▁herself
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- ▁damn
- ▁mix
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- ▁remove
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- ▁quarter
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- ▁correct
- ▁throw
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- ▁aziz
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- ▁fruit
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- é
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- ▁france
- ▁twelve
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- ▁stopped
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- ▁honest
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- ▁vacation
- ▁cheese
- ▁ahead
- ▁sami
- ▁focus
- ▁scared
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- ▁barely
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- ▁body
- ▁continue
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- ▁period
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- ▁remind
- ▁university
- ▁downtown
- ▁sugar
- ▁twice
- ▁women
- ▁couple
- ▁everyday
- ▁condition
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- ▁nobody
- ▁complete
- ▁stomach
- ▁account
- ▁september
- ▁choose
- ▁bottle
- ▁figure
- ▁instead
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- '0'
- '1'
- '3'
- '2'
- '5'
- '7'
- '4'
- '9'
- '8'
- /
- °
- '6'
- è
- $
- ï
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- غ
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- وم
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- ذ
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- ▁الج
- لك
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- سم
- ▁الح
- مت
- ▁الت
- ▁بعد
- اج
- عد
- ▁انشا
- وش
- لت
- ▁وين
- ث
- ▁ولا
- ▁باش
- ▁فيها
- نت
- ▁إ
- ▁الأ
- ▁الف
- ▁إم
- ▁واحد
- ▁ألو
- ▁عندي
- ▁أك
- ▁خل
- ▁وي
- ▁تعمل
- أ
- ▁ريت
- ▁وأ
- ▁تعرف
- بت
- ▁الع
- ▁مشيت
- ▁وه
- ▁حاصيلو
- ▁بالل
- ▁نعمل
- ▁غ
- ▁تجي
- ▁يجي
- ▁كيفاش
- ▁عملت
- ظ
- اك
- ▁هاو
- ▁اش
- ▁قد
- ▁نق
- ▁د
- ▁زادا
- ▁فيه
- رة
- ▁بر
- ▁الش
- ▁ز
- ▁كيما
- ▁الا
- ند
- عم
- ▁نح
- ▁بنتي
- ▁نمشي
- ▁عليك
- ▁نعرفش
- ▁كهو
- ▁وم
- ▁ط
- تي
- ▁خير
- ▁آ
- مش
- ▁عليه
- له
- حت
- ▁إيا
- ▁أحنا
- ▁تع
- الا
- عب
- ▁ديما
- ▁تت
- ▁جو
- ▁مالا
- ▁أو
- ▁قلتلك
- ▁معنتها
- لنا
- ▁شكون
- ▁تحب
- بر
- ▁الر
- ▁وا
- ▁الق
- اء
- ▁عل
- ▁البارح
- ▁وخ
- ▁سافا
- ▁هوما
- ▁ولدي
- ▁
- ▁نعرف
- يف
- رت
- ▁وب
- ▁روح
- ▁علاش
- ▁هاذاك
- ▁رو
- وس
- ▁جا
- ▁كيف
- طر
- ▁غادي
- يكا
- عمل
- ▁نحب
- ▁عندك
- ▁وما
- ▁فر
- اني
- ▁قلتله
- ▁الط
- فر
- ▁دار
- ▁عليها
- ▁يعمل
- ▁نت
- ▁تح
- باح
- ▁ماهو
- ▁وكل
- ▁وع
- قت
- ▁فهمتك
- عر
- ▁وس
- ▁تر
- ▁سي
- يلة
- ▁قلت
- ▁رمضان
- صل
- ▁آما
- ▁الواحد
- ▁بيه
- ▁ثلاثة
- ▁فهمتني
- ▁ها
- بط
- ▁مازال
- قل
- ▁بالك
- ▁معناتها
- ▁ور
- ▁قلتلها
- ▁يس
- رب
- ▁ام
- ▁وبعد
- ▁الث
- ▁وإنت
- ▁بحذا
- ▁لازم
- ْ
- ▁بن
- قرا
- سك
- ▁يت
- خل
- ▁فه
- عت
- ▁هاك
- ▁تق
- ▁قبل
- ▁وك
- ▁نقول
- ▁الز
- حم
- ▁عادش
- حكي
- وها
- بة
- نس
- طل
- ▁علاه
- ذا
- ▁سا
- ▁طل
- الي
- ▁يق
- ▁دو
- حوا
- حد
- ▁نشوف
- نة
- ▁لي
- ▁تك
- ▁نا
- ▁هاذ
- ▁خويا
- ▁المر
- ▁وينك
- ▁البر
- ▁أتو
- ينا
- ▁حل
- ولي
- ▁ثم
- ▁عم
- ▁آي
- ▁قر
- از
- ▁وح
- كش
- بعة
- ▁كيفاه
- ▁نع
- ▁الحمدلله
- ▁ياسر
- ▁الخ
- ▁معاك
- ▁معاه
- ▁تقول
- دة
- ▁حكاية
- تش
- ▁حس
- ▁غدوا
- ▁بالحق
- روا
- وز
- ▁تخ
- ▁العيد
- رجع
- ▁بالي
- ▁جات
- ▁وج
- حة
- ▁وش
- ▁آخر
- ▁طا
- ▁مت
- لقا
- تك
- ▁مس
- ▁راني
- كون
- ▁صاحب
- ▁هاكا
- ▁قول
- ▁عر
- ▁عنده
- ▁يلزم
- ▁هاذا
- ▁يخ
- ▁وقتاش
- ▁وقت
- بع
- ▁العش
- ▁هاذي
- هاش
- ينة
- ▁هاذاكا
- عطي
- ▁تنج
- ▁باهية
- نيا
- فت
- ▁يحب
- ▁تف
- ▁أهلا
- وف
- ▁غدوة
- ▁بيك
- ▁بد
- عن
- ▁در
- ▁ننج
- هار
- ▁الحكاية
- مون
- وق
- ▁نورمال
- ▁عندها
- خر
- ▁بو
- ▁حب
- ▁آكا
- ▁وف
- ▁هاذيكا
- ▁ديجا
- ▁وق
- ▁طي
- لتل
- بعث
- ▁تص
- رك
- ▁مانيش
- ▁العادة
- ▁شوف
- ضر
- ▁يمشي
- ▁نعملوا
- ▁عرفت
- ▁زال
- ▁متع
- ▁عمل
- ▁بيها
- ▁نحكي
- اع
- ▁نج
- معة
- ▁والكل
- عناها
- ▁يعي
- ▁نجي
- ستن
- ▁هاذيك
- ▁عام
- ▁فلوس
- قة
- تين
- ▁بالقدا
- لهم
- ▁تخدم
- ▁ٱ
- ▁شيء
- ▁راهي
- ▁جاب
- ولاد
- ابل
- ▁ماك
- عة
- ▁نمشيوا
- وني
- شري
- بار
- انس
- ▁وقتها
- ▁جديد
- ▁يز
- ▁كر
- ▁حاسيلو
- ▁شق
- ▁اه
- ▁سايي
- ▁انشالل
- رج
- مني
- ▁بلا
- ▁صحيح
- ▁غير
- ▁يخدم
- مان
- وكا
- ▁عند
- ▁قاعدة
- ▁تس
- ربة
- ▁راس
- ▁حط
- ▁نكل
- تني
- ▁الو
- سيون
- ▁عندنا
- ▁لو
- ▁ست
- صف
- ▁ض
- ▁كامل
- ▁نخدم
- ▁يبدا
- ▁دونك
- ▁أمور
- رات
- ▁تونس
- بدا
- ▁تحكي
- ▁سو
- ▁جاي
- ▁وحدة
- ▁ساعة
- حنا
- ▁بكري
- ▁إل
- ▁وبر
- ▁كم
- ▁تبدا
- ارة
- ادي
- رق
- لوا
- ▁يمكن
- ▁خاط
- ▁وص
- جين
- ▁هاذاي
- ▁هز
- قد
- ▁قل
- ▁وكهو
- ▁نص
- ▁دي
- لقى
- ▁وأنا
- سين
- ▁يح
- ▁ماشي
- ▁شو
- ▁خذيت
- امات
- ▁كنت
- خرج
- ▁لقيت
- رتاح
- كس
- ▁حاجات
- ▁مريق
- ▁مل
- ليفون
- اوا
- ▁شفت
- ▁عاملة
- ▁تن
- ▁والا
- سأل
- ▁حد
- ▁قاللك
- ▁العباد
- ▁عالاخ
- ▁وآك
- ▁ماني
- ▁ناخذ
- ▁حم
- ▁الإ
- ▁ماضي
- ▁ث
- الة
- ▁أخرى
- رين
- ▁تشوف
- ▁نخرج
- ▁أربعة
- ▁ألف
- نيش
- ▁هاي
- آ
- ▁فيك
- رشة
- ولة
- فلة
- ▁بابا
- ▁أما
- ▁روحي
- ▁فيهم
- ▁رج
- ▁ليك
- ونس
- يرة
- ▁وأكهو
- ندي
- ▁صار
- شك
- ▁نرو
- ▁آكهو
- ▁تش
- ▁غاديكا
- ▁معاها
- ▁لب
- ▁أذاكا
- ▁آني
- ▁يوم
- عملوا
- ▁نقعد
- دوا
- ▁عد
- سمع
- متني
- ▁الخدمة
- ▁مازلت
- ▁قعدت
- ايا
- ▁برك
- قعد
- ▁خرجت
- ضح
- ▁قالل
- ▁يقول
- ▁وفي
- ▁حق
- ختي
- ▁يعني
- خدم
- ▁جيت
- ▁نرمال
- طف
- ▁عجب
- ▁تقعد
- ▁مشينا
- اية
- ▁خدمة
- لدي
- روف
- ▁الفطر
- ▁مشكل
- ▁سل
- ▁وآنا
- الط
- ▁بالس
- ▁هانا
- ▁أوه
- ▁أذيكا
- ▁وإ
- ▁عليهم
- ▁حالة
- جت
- قضي
- ▁لق
- ▁ونصف
- سعة
- عطيه
- عاو
- خانة
- ▁مخ
- ▁شبيك
- بيعة
- ▁أهوك
- يني
- ▁تعد
- ▁خال
- ▁قريب
- ▁راك
- ▁قالت
- ▁لتو
- ▁أكثر
- اعة
- ▁يظهرلي
- ▁ماشية
- سمعني
- ▁نسيت
- ▁ينج
- ▁الحمدلل
- هدي
- ▁وشن
- ▁تطي
- ▁هنا
- ▁نسمع
- ▁إنتوما
- ▁نحكيلك
- ▁قاعد
- ▁اسمعني
- خرين
- إ
- ماعة
- ▁بالر
- ▁دا
- ▁عمر
- ▁نشري
- ▁قهوة
- ▁تبارك
- ▁صب
- ▁مشات
- غر
- ▁شريت
- ▁عامل
- ▁زوج
- ثنين
- ▁برب
- ريق
- ▁نكم
- ▁لم
- بيب
- ▁مياة
- ▁مالل
- ▁قعد
- ▁سخون
- قس
- ▁وحده
- ▁اسمع
- ▁خمسة
- ▁غالي
- ▁الأو
- رلي
- ▁العظيم
- ▁ترو
- تهم
- كري
- ▁نجيب
- ▁جملة
- قول
- ▁قلتلي
- ▁إيجا
- ▁يقعد
- ▁إيام
- ▁يعطيك
- ▁نخل
- ▁دب
- يمة
- رهبة
- ▁نهز
- ▁محم
- ▁بين
- غار
- ▁نحنا
- ▁بون
- ▁الغ
- ▁شهر
- ▁بار
- رقة
- ▁نطي
- ئ
- ترو
- ▁ملا
- ▁الكرهبة
- ▁باه
- ▁عالإخ
- ▁عباد
- ▁بلاصة
- ▁مشى
- بيع
- ▁نفس
- ▁عملنا
- ▁واح
- ▁أحلاه
- ▁بحذاك
- ▁لأ
- ▁دخ
- باب
- ▁ودر
- ▁غالب
- ▁ناكل
- ▁مثلا
- ء
- ▁راقد
- ▁تفر
- ▁الوقت
- ▁تاخذ
- حذا
- نتر
- ▁نبدا
- ▁حال
- ▁مريم
- الم
- ▁جمعة
- رجول
- ▁معايا
- ▁تخرج
- ▁باس
- ▁ساعات
- ▁عندهم
- ▁نتفر
- مسة
- ▁الجمعة
- بعين
- ▁أكاهو
- ▁ميش
- مراة
- ▁خذا
- ▁ظ
- ▁سيدي
- ▁معاي
- ▁شبيه
- ▁حكا
- ▁سف
- ▁بعضنا
- ▁بالض
- ▁ليلة
- ▁زعما
- ▁الحق
- مضان
- ▁صعيب
- ▁قالتلك
- ً
- ملة
- ▁بق
- عرف
- لاطة
- ▁خرج
- ▁أخت
- ▁تقوللي
- ▁معانا
- ▁صغير
- ▁إسمه
- ▁بعض
- ▁العام
- ▁علينا
- ▁يتع
- ▁فاش
- ▁شع
- ▁معاهم
- ▁يسالش
- ▁لهنا
- ▁سمعت
- ▁البار
- ▁نتصو
- ▁الاخ
- ▁وكان
- وبة
- دمة
- ▁كون
- ▁مبعد
- ▁تسمع
- ▁بعيد
- ▁تاكل
- ▁نلقا
- لامة
- لاثة
- ▁ذ
- ▁تحس
- ▁الواح
- ▁لدار
- ▁فاتت
- ▁تاو
- ▁أحوالك
- ▁عاملين
- ▁كبيرة
- عجب
- ▁بنت
- ▁بيدي
- ▁حكيت
- ▁تحط
- ▁مسكينة
- ▁هاذوكم
- ▁نزيد
- لاث
- ▁عشرة
- ▁عيني
- ▁تعب
- ▁ياكل
- ▁وزيد
- ▁طول
- ▁حمدلله
- ▁وقتاه
- ▁معناه
- ▁وآش
- ▁ووه
- ▁وواحد
- ▁نشوفوا
- ▁عيد
- ▁بصراحة
- ▁بحذانا
- ▁قاعدين
- ▁راجل
- ▁وحدي
- ▁وعشرين
- ▁لين
- ▁خايب
- ▁قالتله
- ▁تهز
- عيد
- ▁كبير
- ▁يعرف
- ▁عارف
- ▁الفلوس
- ▁زايد
- ▁خدمت
- ▁هاذوما
- ▁سلاطة
- ▁فارغة
- ▁ساعتين
- ▁تبد
- ▁راو
- ▁مائة
- ▁بعضهم
- ▁ظاهرلي
- ▁الفازة
- كتب
- ▁القهوة
- سبوك
- ▁زاد
- ▁ضرب
- حكيلي
- ▁فوق
- ▁عاود
- ▁راي
- ▁ومبعد
- ▁حوايج
- ▁دخلت
- ▁يقوللك
- ▁زيد
- ▁زلت
- لفزة
- ▁وقال
- ▁يهب
- ▁يلزمني
- ▁الحمد
- ▁أذي
- طبيعت
- ▁دورة
- ▁عالأقل
- ▁آذاك
- ▁وبال
- ▁الجاي
- عطيني
- ▁ياخذ
- ▁احكيلي
- ▁نهبط
- ▁رقدت
- بلاصة
- ▁عزيز
- ▁صغار
- ▁أقسم
- ▁جيب
- ▁وصلت
- ▁أحوال
- ▁جيست
- ▁جماعة
- سئل
- ▁خوذ
- ▁يهز
- ▁الأخرى
- ▁آلاف
- ▁إسمع
- ▁الحقيقة
- ▁ناقص
- ▁حاط
- ▁موجود
- عباد
- ▁آذيك
- ▁خارج
- ▁الخير
- ▁البنات
- بقى
- ▁طرف
- ▁سينون
- ▁ماذاب
- ▁البحر
- ▁نرقد
- مدلله
- ▁إيجى
- ▁خالتي
- ▁فازة
- ▁بريك
- ▁شريبتك
- ▁تطلع
- ؤ
- ▁المشكلة
- ▁طري
- ▁مادام
- ▁طلبت
- ▁يلعب
- ▁نعاود
- ▁وحدك
- ▁ظاهر
- ٱ
- ژ
- ٍ
- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
model_conf:
    asr_weight: 0.3
    mt_weight: 0.0
    mtlalpha: 1.0
    lsm_weight: 0.1
    length_normalized_loss: false
use_preprocessor: true
token_type: bpe
src_token_type: bpe
bpemodel: data/token_list/tgt_bpe_unigram1000/bpe.model
src_bpemodel: data/token_list/src_bpe_unigram1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
    n_fft: 512
    win_length: 400
    hop_length: 160
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: global_mvn
normalize_conf:
    stats_file: exp/st_stats_raw_bpe1000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: transformer
encoder_conf:
    input_layer: conv2d
    num_blocks: 12
    linear_units: 2048
    dropout_rate: 0.1
    output_size: 256
    attention_heads: 4
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    input_layer: embed
    num_blocks: 6
    linear_units: 2048
    dropout_rate: 0.1
extra_asr_decoder: transformer
extra_asr_decoder_conf:
    input_layer: embed
    num_blocks: 2
    linear_units: 2048
    dropout_rate: 0.1
extra_mt_decoder: transformer
extra_mt_decoder_conf:
    input_layer: embed
    num_blocks: 2
    linear_units: 2048
    dropout_rate: 0.1
required:
- output_dir
- src_token_list
- token_list
version: 0.10.6a1
distributed: true
```


</details>


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit},
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```


model name: brianyan918_iwslt22_dialect_st_transformer_fisherlike_4gpu_bbins16m_fix


model author: espnet


tags: noinfo, dataset:iwslt22_dialect, arxiv:1804.00015, espnet, audio, speech-translation, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- fsc
license: cc-by-4.0


## ESPnet2 SLU (Entity Classification) pretrained model


### `siddhana/slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best`


♻ Imported from https://zenodo.org/record/5590204


This model was trained by siddhana using fsc/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: siddhana_slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best


task: automatic-speech-recognition


tags: en, dataset:fsc, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/csmsc_conformer_fastspeech2`


♻ Imported from https://zenodo.org/record/4031955/


This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


model name: kan-bayashi_csmsc_conformer_fastspeech2


model author: espnet


task: text-to-speech


tags: zh, dataset:csmsc, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`


♻ Imported from https://zenodo.org/record/4436450/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


### `kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`


♻ Imported from https://zenodo.org/record/4381102/


### Citing ESPnet


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave


model author: espnet


task: text-to-speech


tags:


- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/csmsc_tacotron2`


♻ Imported from https://zenodo.org/record/3969118/


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_csmsc_tacotron2


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- sinhala
license: cc-by-4.0


## ESPnet2 ASR pretrained model


### `espnet/Karthik_sinhala_asr_train_asr_transformer`


This model was trained by Karthik using sinhala/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```


or arXiv:


model name: Karthik_sinhala_asr_train_asr_transformer


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:sinhala, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jvs
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest`


♻ Imported from https://zenodo.org/record/5521494/


This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


model name: kan-bayashi_jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest


task: text-to-speech


tags: ja, dataset:jvs, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_gst_fastspeech2`


♻ Imported from https://zenodo.org/record/4036266/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_vctk_gst_fastspeech2


model author: espnet


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


### `kan-bayashi/jsut_fastspeech2_accent_with_pause`


♻ Imported from https://zenodo.org/record/4436450/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_jsut_fastspeech2_accent_with_pause


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


### Citing ESPnet


or arXiv:


model name: kan-bayashi_vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


model author: espnet


- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0


### `kan-bayashi/ljspeech_fastspeech2`


♻ Imported from https://zenodo.org/record/4036272/


This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


model name: kan-bayashi_ljspeech_fastspeech2


tags: en, dataset:ljspeech, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: ja
datasets:
- csj
license: cc-by-4.0


## Example ESPnet2 ASR model


### `kan-bayashi/csj_asr_train_asr_transformer_raw_char_sp_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4037458/


This model was trained by kan-bayashi using csj/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_csj_asr_train_asr_transformer_raw_char_sp_valid.acc.ave


model author: espnet


task: automatic-speech-recognition


tags: ja, dataset:csj, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: en_unnorm
datasets:
- spgispeech
license: cc-by-4.0


### `Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4585558/


This model was trained by Shinji Watanabe using spgispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


model name: Shinji_Watanabe_spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave


tags: en_unnorm, dataset:spgispeech, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: su
datasets:
- su_openslr36
license: cc-by-4.0


## ESPnet2 ASR pretrained model


### `su_openslr36`


♻ Imported from https://zenodo.org/record/5090135/


This model was trained by su_openslr36 using su_openslr36/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


or arXiv:


model name: su_openslr36


model author: espnet


task: automatic-speech-recognition


tags: su, dataset:su_openslr36, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best`


♻ Imported from https://zenodo.org/record/3986241/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best


task: text-to-speech


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


### `kan-bayashi/jsut_conformer_fastspeech2_accent`


♻ Imported from https://zenodo.org/record/4381102/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_jsut_conformer_fastspeech2_accent


model author: espnet


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: ja
datasets:
- laborotv
license: cc-by-4.0


## Example ESPnet2 ASR model


### `Shinji_Watanabe/laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4304245/


This model was trained by Shinji Watanabe using laborotv/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


model name: Shinji_Watanabe_laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave


task: automatic-speech-recognition


tags: ja, dataset:laborotv, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- slue-voxceleb
license: cc-by-4.0


## ESPnet2 ASR model


### `espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best`


This model was trained by Siddhant using slue-voxceleb recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```bash
cd espnet
git checkout 17758ad804fd7c4b6f88ef5601f475a241dc4605
pip install -e .
cd egs2/slue-voxceleb/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best
```


<!-- Generated by scripts/utils/show_asr_result.sh -->


# RESULTS


## Environments


- date: `Tue Dec 28 12:28:28 EST 2021`
- python version: `3.9.5 (default, Jun  4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.3a2`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `6bf3c2a4f138d35331634d2e879bbc5c32a5266e`
  - Commit date: `Mon Dec 22 15:41:32 EST 2021`


## Using Conformer based encoder and Transformer based decoder with spectral augmentation and predicting transcript along with intent


- ASR config: [conf/train_asr.yaml](conf/tuning/train_asr_conformer.yaml)
- token_type: word


|dataset|Snt|Intent Classification Accuracy (%)|Intent Classification Macro F1 (%)|
|---|---|---|---|
|inference_asr_model_valid.acc.ave_10best/devel|955|80.2|29.7|


### Detailed Classification Report


## ASR config


<details><summary>expand</summary>


```
config: conf/tuning/train_asr_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/train/wav.scp
    - speech
    - sound
-   - dump/raw/train/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/devel/wav.scp
    - speech
    - sound
-   - dump/raw/devel/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.0002
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ▁i
- s
- ▁and
- ''''
- ▁the
- ▁a
- ▁to
- ▁it
- Neutral
- ▁you
- ▁that
- ▁of
- t
- ing
- ▁in
- ▁was
- ed
- ▁uh
- ▁know
- e
- m
- ▁he
- y
- er
- ▁so
- ▁we
- re
- a
- o
- d
- ▁um
- i
- ▁s
- c
- ▁like
- n
- ▁is
- ▁be
- ▁f
- ▁but
- ▁c
- Positive
- en
- l
- ve
- ▁just
- ▁m
- st
- ▁they
- le
- an
- ▁on
- ▁p
- u
- ▁my
- ar
- p
- ▁this
- ▁for
- ▁b
- ▁think
- in
- ▁with
- g
- or
- ▁h
- r
- ly
- w
- ▁me
- ▁d
- ▁e
- ▁have
- ▁she
- it
- ▁t
- ▁what
- b
- ▁st
- al
- es
- ▁there
- ▁really
- ic
- ▁g
- ▁as
- ▁w
- ▁l
- ▁do
- ll
- v
- ▁all
- at
- 'on'
- as
- ▁about
- h
- ▁not
- ▁re
- ▁o
- ▁at
- k
- ▁don
- ▁had
- ▁when
- ou
- ent
- is
- ra
- ▁who
- ri
- ▁go
- se
- f
- ▁out
- ▁get
- ▁an
- ▁people
- nd
- ▁kind
- ▁very
- ce
- ▁because
- ▁are
- ion
- ▁some
- et
- ▁can
- ge
- ▁or
- me
- ▁up
- ▁n
- ▁if
- ▁no
- ▁one
- ▁were
- ct
- ▁mean
- ad
- ▁time
- ▁ch
- ▁then
- ro
- ▁ex
- ▁mo
- ▁her
- ▁every
- ▁would
- ▁co
- ▁work
- ir
- ▁sh
- ay
- ▁se
- ol
- ver
- ▁su
- ▁got
- ▁k
- th
- ▁love
- ▁from
- ld
- ation
- ▁him
- ▁said
- ▁how
- ▁well
- ▁lot
- ▁show
- ch
- ard
- ie
- ▁pro
- ▁de
- ▁gonna
- ▁bo
- ▁say
- ▁see
- ▁li
- one
- ▁his
- ther
- ▁been
- ur
- ▁any
- ▁great
- ▁
- ▁yeah
- pe
- ▁which
- ▁come
- ▁them
- ot
- ▁play
- ab
- ite
- ▁way
- ally
- id
- gh
- ▁r
- ▁sc
- our
- x
- mp
- ers
- ong
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- ▁your
- ss
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- ▁sort
- ▁am
- am
- and
- ▁make
- ant
- ▁thing
- ▁ha
- ▁te
- ▁has
- ess
- ▁v
- ▁something
- ▁back
- ▁where
- ▁things
- red
- ▁al
- ut
- el
- ight
- ment
- un
- ive
- ▁th
- ▁le
- il
- ▁j
- op
- ▁more
- ▁ro
- ill
- ▁fi
- ies
- ▁much
- ck
- ▁ne
- ▁wh
- ▁always
- ▁act
- ine
- pp
- z
- ▁now
- ▁con
- thing
- ▁us
- body
- ▁want
- ▁other
- ort
- ice
- ▁doing
- ▁sa
- ▁feel
- ow
- ▁int
- ne
- ▁these
- ▁could
- ▁good
- ▁cause
- Negative
- ▁actually
- ▁wr
- ▁little
- ain
- ▁being
- ▁look
- ▁into
- ere
- ul
- ▁our
- ▁guy
- ▁first
- ud
- ▁by
- ▁fun
- ▁qu
- ▁didn
- us
- ity
- ▁jo
- od
- ▁u
- ▁part
- ▁off
- ▁pre
- ▁right
- ▁film
- ▁start
- ok
- ▁two
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- pt
- um
- te
- ▁movie
- ▁going
- ff
- nder
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- ▁ag
- ▁en
- ▁try
- ful
- im
- ays
- ▁life
- ▁different
- ach
- are
- ▁di
- ist
- ▁oh
- au
- ▁po
- nt
- ▁com
- all
- ▁lo
- om
- ▁real
- ▁y
- ame
- ▁went
- ry
- ber
- ▁even
- ci
- ▁ho
- ▁years
- ▁their
- ▁happen
- ure
- self
- per
- ▁pl
- ▁those
- ble
- 'no'
- ▁day
- ▁take
- ▁does
- ien
- ▁br
- be
- wn
- ▁thought
- ▁fe
- ght
- ▁tr
- ▁story
- ty
- ▁down
- ous
- ish
- ▁wom
- ▁wanna
- ▁put
- ▁through
- ide
- ▁ab
- ▁new
- ▁also
- ▁big
- ▁call
- ▁around
- ▁character
- ▁read
- iz
- ▁came
- act
- ily
- ath
- ag
- ree
- ▁per
- ▁will
- ▁mu
- ▁talk
- ▁over
- ▁friend
- atch
- ▁bl
- ade
- ▁world
- ▁many
- ▁sp
- sic
- ▁cl
- ▁bit
- ▁man
- ace
- ▁person
- ft
- ip
- ▁than
- ▁wanted
- ▁may
- ven
- ick
- ious
- ▁mar
- ▁before
- ▁rel
- j
- ting
- ▁set
- sh
- ep
- ▁un
- ue
- ▁aw
- ▁find
- ▁kid
- tain
- ▁such
- ter
- ▁end
- ▁tw
- ind
- aking
- ▁after
- ▁fam
- ars
- ig
- ore
- ▁bec
- ak
- art
- reat
- ust
- rou
- ack
- ▁ye
- ould
- ime
- itt
- ▁gu
- qu
- ose
- fe
- ▁wor
- lf
- alk
- ▁charact
- ▁mov
- out
- ich
- ▁happ
- ▁thou
- ith
- <mixed>
- rom
- ake
- ▁diff
- ▁char
- na
- round
- ory
- ink
- ually
- ▁gon
- ▁pe
- right
- ody
- ah
- rie
- riend
- now
- so
- ause
- ▁fil
- ▁pers
- fore
- very
- ▁differe
- rough
- q
- ▁fir
- anna
- ways
- ':'
- '&'
- fter
- <sos/eos>
transcript_token_list: null
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
    extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
    output_size: 512
    attention_heads: 8
    linear_units: 2048
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    normalize_before: true
    macaron_style: true
    pos_enc_layer_type: rel_pos
    selfattention_layer_type: rel_selfattn
    activation_type: swish
    use_cnn_module: true
    cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    attention_heads: 8
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
postdecoder: null
postdecoder_conf: {}
required:
- output_dir
- token_list
version: 0.10.3a2
distributed: false
```


</details>


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit},
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```


model name: siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:slue-voxceleb, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
inference: false


# ESPnet2 ASR pretrained model


## `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best, fs=16k, lang=en`


♻ Imported from <https://zenodo.org/record/3966501#.YOAOUZozZH5>


This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/).


### Python API


```text
See https://github.com/espnet/espnet_model_zoo
```


### Evaluate in the recipe


```python
# coming soon
```


### Results


```bash
# RESULTS
## Environments
- date: `Tue Jul 21 07:58:39 EDT 2020`
- python version: `3.7.3 (default, Mar 27 2019, 22:11:17)  [GCC 7.3.0]`
- espnet version: `espnet 0.8.0`
- pytorch version: `pytorch 1.4.0`
- Git hash: `75db853dd26a40d3d4dd979b2ff2457fbbb0cd69`
  - Commit date: `Mon Jul 20 10:49:12 2020 -0400`

## asr_train_asr_transformer_e18_raw_bpe_sp
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|54402|97.9|1.8|0.2|0.2|2.3|28.2|
|decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|54402|97.9|1.9|0.2|0.3|2.4|29.5|
|decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|50948|94.6|4.7|0.7|0.7|6.0|46.6|
|decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|50948|94.4|5.0|0.5|0.8|6.3|47.5|
|decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|52576|97.7|2.0|0.3|0.3|2.6|30.4|
|decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|52576|97.7|2.0|0.2|0.3|2.6|30.1|
|decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|52343|94.5|4.8|0.7|0.7|6.2|49.7|
|decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|52343|94.3|5.1|0.6|0.8|6.5|50.3|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|288456|99.3|0.3|0.3|0.2|0.9|28.2|
|decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|288456|99.3|0.4|0.3|0.2|0.9|29.5|
|decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|265951|97.7|1.2|1.1|0.6|2.9|46.6|
|decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|265951|97.7|1.3|1.0|0.8|3.0|47.5|
|decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|281530|99.3|0.3|0.4|0.3|1.0|30.4|
|decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|281530|99.4|0.3|0.3|0.3|0.9|30.1|
|decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|272758|97.8|1.1|1.1|0.7|2.9|49.7|
|decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|272758|97.9|1.2|0.9|0.8|2.9|50.3|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|69307|97.2|1.8|1.0|0.4|3.2|28.2|
|decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|69307|97.2|1.9|1.0|0.5|3.3|29.5|
|decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|64239|93.3|4.4|2.2|1.2|7.9|46.6|
|decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|64239|93.2|4.9|1.9|1.5|8.3|47.5|
|decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|66712|97.0|1.9|1.1|0.4|3.3|30.4|
|decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|66712|97.1|1.9|1.0|0.5|3.3|30.1|
|decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|66329|93.1|4.5|2.4|1.0|7.9|49.7|
|decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|66329|93.1|4.8|2.1|1.4|8.3|50.3|
```


### Training config


See full config in [`config.yaml`](./exp/asr_train_asr_transformer_e18_raw_bpe_sp/config.yaml)


```yaml
config: conf/tuning/train_asr_transformer_e18.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_e18_raw_bpe_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 3
local_rank: 3
dist_master_addr: localhost
dist_master_port: 33643
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```


model name: shinji-watanabe-librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:librispeech, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- wsj
license: cc-by-4.0


## Example ESPnet2 ASR model


### `kamo-naoyuki/wsj`


♻ Imported from https://zenodo.org/record/4003381/


This model was trained by kamo-naoyuki using wsj/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kamo-naoyuki_wsj


tags: en, dataset:wsj, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- fsc
license: cc-by-4.0


## ESPnet2 SLU pretrained model


### `siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best`


♻ Imported from https://zenodo.org/record/5590204


This model was trained by siddhana using fsc/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


model name: siddhana_fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:fsc, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/csmsc_fastspeech2`


♻ Imported from https://zenodo.org/record/4031953/


This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


model name: kan-bayashi_csmsc_fastspeech2


task: text-to-speech


tags: zh, dataset:csmsc, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody`


♻ Imported from https://zenodo.org/record/5499066/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_conformer_fastspeech2_transformer_prosody


model author: espnet


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- totonac
license: cc-by-4.0


## ESPnet2 ASR model


### `espnet/ftshijt_espnet2_asr_totonac_transformer`


This model was trained by ftshijt using totonac recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```bash
cd espnet

pip install -e .
cd els/totonac/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_totonac_transformer
```


<!-- Generated by scripts/utils/show_asr_result.sh -->


# RESULTS


## Environments


- date: `Sun Nov  7 09:22:09 EST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58)  [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: ``
  - Commit date: ``


## asr_train_asr_transformer_specaug_raw_bpe250_sp


### WER


### CER


### TER


## ASR config


<details><summary>expand</summary>


```
config: conf/tuning/train_asr_transformer_specaug.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_specaug_raw_bpe250_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe250_sp/train/speech_shape
- exp/asr_stats_raw_bpe250_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe250_sp/valid/speech_shape
- exp/asr_stats_raw_bpe250_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - /tmp/jiatong-7359.okvPvI3Z/raw/train_sp/wav.scp
    - speech
    - kaldi_ark
-   - /tmp/jiatong-7359.okvPvI3Z/raw/train_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - /tmp/jiatong-7359.okvPvI3Z/raw/dev/wav.scp
    - speech
    - kaldi_ark
-   - /tmp/jiatong-7359.okvPvI3Z/raw/dev/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 1.0
scheduler: noamlr
scheduler_conf:
    warmup_steps: 4000
token_list:
- <blank>
- <unk>
- ':'
- ▁N
- NI
- N
- ▁IYMA
- ▁NA
- NA
- ▁WA
- WA
- ▁
- ''''
- KA
- ▁MA
- MA
- T
- ▁XA
- TA
- NCHU
- WI
- ▁LI
- ▁NI
- PA
- YI
- ▁PUS
- K
- ▁PI
- ▁X
- S
- ▁TA
- YA
- ▁LA
- Q
- QA
- TI
- ▁KA
- QO
- W
- ▁KAH
- ▁PALA
- H
- X
- XA
- ▁KI
- A
- LH
- I
- LA
- ▁CHA
- ▁A
- ▁XLI
- ▁LHI
- U
- ▁K
- KANI
- KU
- Y
- ▁LU
- Á
- ▁CHU
- O
- KI
- ▁KIWI
- NTLA
- ▁TLA
- M
- ▁TAWA
- ▁TI
- ▁S
- WANI
- CHA
- LHI
- LI
- ▁TU
- ▁PALHA
- Í
- ▁CHANÁ
- ▁KILHWAMPA
- KÁN
- ▁WAYMA
- E
- SA
- ▁E
- ▁LHU
- LHA
- PU
- ▁LHA
- ▁PA
- ▁LAK
- ▁ANTA
- ▁KITI
- NCHÚ
- SI
- TLA
- PI
- ▁KINI
- CHI
- ▁PEROH
- ▁PU
- QÓ
- QALHCHIWINA
- TU
- ▁TLHA
- ▁WI
- NÁ
- ▁KAN
- ▁NAYI
- CH
- 'NO'
- ▁U
- TSA
- MÁ
- NQO
- ▁ANA
- ▁LIKWA
- ▁XTA
- J
- ▁QALH
- TO
- TÁ
- ▁USA
- ▁PORQUE
- ▁MI
- L
- ▁TAWÁ
- XI
- LHAQAPASA
- P
- CHIWI
- WÁ
- NTI
- ▁JKA
- Ú
- NTLHA
- R
- TSI
- C
- STA
- ▁LH
- LHU
- MPI
- ▁I
- ▁NILH
- ▁KATSI
- ▁LHAK
- MAKLHAKASKI
- ▁WANIKÁN
- ▁WIXI
- ▁TSI
- KÚ
- NÍ
- ▁PAKS
- NU
- TLHA
- YÁ
- KUCHAN
- XAQATLI
- ▁MAX
- ▁LAQAPASA
- ▁LAQ
- QALH
- KATSI
- Ó
- LAQAPASA
- ▁J
- ▁QAMA
- NTU
- MI
- KIWI
- ▁KIN
- ▁XANAT
- ▁CHI
- JA
- ▁IY
- ▁TSU
- MAKLAKAS
- ▁MAQA
- LÁ
- ▁KATSIYA
- ▁TLANKA
- ▁STAK
- ▁XLA
- ▁LHIKWA
- ▁SQA
- ▁P
- TAHNA
- ▁TLAQ
- ▁JKATSI
- MAKLAKASKINKA
- YÁW
- WATIYA
- CHÁ
- ▁IPORQUEI
- ▁AKXNI
- TSU
- ▁TSINÓ
- ▁STAKA
- ▁AKXNÍ
- LAKATA
- KATSÍ
- ▁XALHAK
- TLAWAYA
- SPUT
- ▁XATAWA
- QALHCHIWI
- PÁ
- JU
- ▁XAXANAT
- ▁PÉREZ
- ▁AKTSU
- ▁JKI
- NTÚ
- ▁KATSIYÁ
- ▁IESTEI
- LAQAPASÁ
- ▁MASKI
- ▁LAQSQATÁ
- ▁TLHANKA
- ▁WANIKANI
- ▁LÓPEZ
- MAKLAKASKINKÁN
- ▁ANTÁ
- ▁TACHIWÍ
- ▁SEBAST
- ▁CANO
- ▁XKUTNI
- ▁UKXILH
- TANKAH
- LAKASKINQO
- LAKAPASTAK
- ▁XCHACHAT
- TAKAWANÍ
- ▁TLÁ
- ▁TSINOH
- KAXTLAWA
- ▁NÚÑEZ
- ▁XLAKASKINKA
- ▁WÁTIYA
- ONCE
- Z
- É
- D
- Ñ
- V
- F
- G
- '1'
- B
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram250/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_bpe250_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: transformer
encoder_conf:
    input_layer: conv2d
    num_blocks: 12
    linear_units: 2048
    dropout_rate: 0.1
    output_size: 256
    attention_heads: 4
    attention_dropout_rate: 0.0
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    input_layer: embed
    num_blocks: 6
    linear_units: 2048
    dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```


</details>


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit},
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```


model name: ftshijt_espnet2_asr_totonac_transformer


model author: espnet


task: automatic-speech-recognition


tags: noinfo, dataset:totonac, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tacotron2_accent`


♻ Imported from https://zenodo.org/record/4381098/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_tacotron2_accent


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0


### `kan-bayashi/ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`


♻ Imported from https://zenodo.org/record/4036268/


This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


model name: kan-bayashi_ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave


model author: espnet


tags: en, dataset:ljspeech, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


## ESPnet2 TTS pretrained model


### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave`


♻ Imported from https://zenodo.org/record/5499026/


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave


task: text-to-speech


### `kan-bayashi/jsut_tacotron2_prosody`


model name: kan-bayashi_jsut_tacotron2_prosody


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best`


♻ Imported from https://zenodo.org/record/3986237/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best


model author: espnet


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- reverb
license: cc-by-4.0


## Example ESPnet2 ASR model


♻ Imported from https://zenodo.org/record/4441309/


This model was trained by kamo-naoyuki using reverb/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


task: automatic-speech-recognition


tags: en, dataset:reverb, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- spgispeech
license: cc-by-4.0


### `Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4585546/


This model was trained by Shinji Watanabe using spgispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


model name: Shinji_Watanabe_spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave


model author: espnet


tags: en, dataset:spgispeech, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tacotron2`


♻ Imported from https://zenodo.org/record/3963886/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_tacotron2


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/csmsc_vits`


♻ Imported from https://zenodo.org/record/5499120/


This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


model name: kan-bayashi_csmsc_vits


model author: espnet


tags: zh, dataset:csmsc, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0


### `kan-bayashi/ljspeech_joint_finetune_conformer_fastspeech2_hifigan`


♻ Imported from https://zenodo.org/record/5498896/


This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan


task: text-to-speech


tags: en, dataset:ljspeech, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/ljspeech_tacotron2`


♻ Imported from https://zenodo.org/record/3989498/


### Citing ESPnet


or arXiv:


model name: kan-bayashi_ljspeech_tacotron2


model author: espnet


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave`


♻ Imported from https://zenodo.org/record/5414980/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


### `kan-bayashi/ljspeech_tts_finetune_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`


### Citing ESPnet


or arXiv:


model name: kan-bayashi_ljspeech_tts_finetune_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave


model author: espnet


tags:


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave`


♻ Imported from https://zenodo.org/record/4433196/


model name: kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave


### `kan-bayashi/jsut_transformer_accent`


♻ Imported from https://zenodo.org/record/4381096/


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_jsut_transformer_accent


task: text-to-speech


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0


## Example ESPnet2 ASR model


♻ Imported from https://zenodo.org/record/4543018/


This model was trained by kamo-naoyuki using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:librispeech, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: kr
datasets:
- ksponspeech
license: cc-by-4.0


## ESPnet2 ASR pretrained model


### `Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.acc.best`


♻ Imported from https://zenodo.org/record/5154341/


This model was trained by Yushi Ueda using ksponspeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: Yushi_Ueda_ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.acc.best


tags: kr, dataset:ksponspeech, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/vctk_multi_spk_vits`


♻ Imported from https://zenodo.org/record/5500759/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


model name: kan-bayashi_vctk_multi_spk_vits


model author: espnet


task: text-to-speech


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/ljspeech_transformer`


♻ Imported from https://zenodo.org/record/4039194/


This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_ljspeech_transformer


tags: en, dataset:ljspeech, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- text-to-speech
language: en
datasets:
- libritts
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/libritts_xvector_vits`


♻ Imported from https://zenodo.org/record/5521416/


This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_libritts_xvector_vits


model author: espnet


task: text-to-speech


tags: en, dataset:libritts, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: ja
datasets:
- jsut
license: cc-by-4.0


## Example ESPnet2 ASR model


### `Hoon_Chung/jsut_asr_train_asr_conformer8_raw_char_sp_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4292742/


This model was trained by Hoon Chung using jsut/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


model name: Hoon_Chung_jsut_asr_train_asr_conformer8_raw_char_sp_valid.acc.ave


task: automatic-speech-recognition


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- aishell
license: cc-by-4.0


### `kamo-naoyuki/aishell_conformer`


♻ Imported from https://zenodo.org/record/4105763/


This model was trained by kamo-naoyuki using aishell/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


model name: kamo-naoyuki_aishell_conformer


model author: espnet


tags: zh, dataset:aishell, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- libritts
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/libritts_gst+xvector_trasnformer`


♻ Imported from https://zenodo.org/record/4409702/


This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi_libritts_gst_xvector_trasnformer


task: text-to-speech


tags: en, dataset:libritts, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- fsc_unseen
license: cc-by-4.0


## ESPnet2 ASR pretrained model


### `siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best`


♻ Imported from https://zenodo.org/record/5655832


This model was trained by siddhana using fsc_unseen/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

```


or arXiv:


model name: siddhana_fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:fsc_unseen, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jvs
license: cc-by-4.0


## ESPnet2 TTS pretrained model


### `kan-bayashi/jvs_jvs010_vits_accent_with_pause`


♻ Imported from https://zenodo.org/record/5432566/


This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


model name: kan-bayashi_jvs_jvs010_vits_accent_with_pause


task: text-to-speech


tags: ja, dataset:jvs, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- hkust
license: cc-by-4.0


## Example ESPnet2 ASR model


### `kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4430974/


This model was trained by kamo-naoyuki using hkust/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kamo-naoyuki_hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.acc.ave


model author: espnet


task: automatic-speech-recognition


tags: zh, dataset:hkust, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- vctk
license: cc-by-4.0


## Example ESPnet2 TTS model


### `kan-bayashi/vctk_xvector_tacotron2`


♻ Imported from https://zenodo.org/record/4394600/


This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


model name: kan-bayashi_vctk_xvector_tacotron2


task: text-to-speech


tags: en, dataset:vctk, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0


## Example ESPnet2 ASR model


### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.ave`


♻ Imported from https://zenodo.org/record/4541452/


This model was trained by kamo-naoyuki using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


model name: kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.ave


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:librispeech, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
inference: false


# ESPnet2 ASR pretrained model


## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en`


This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/).


### Python API


```text
See https://github.com/espnet/espnet_model_zoo
```


### Evaluate in the recipe


```python
# coming soon
```


### Results


```bash
# RESULTS
## Environments
- date: `Fri Aug  6 11:44:39 JST 2021`
- python version: `3.7.9 (default, Apr 23 2021, 13:48:31)  [GCC 5.5.0 20171010]`
- espnet version: `espnet 0.9.9`
- pytorch version: `pytorch 1.7.0`
- Git hash: `0f7558a716ab830d0c29da8785840124f358d47b`
  - Commit date: `Tue Jun 8 15:33:49 2021 -0400`

## asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.5|1.3|0.2|0.2|1.7|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|50948|96.8|2.8|0.4|0.3|3.4|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|52576|98.4|1.4|0.2|0.2|1.8|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|52343|96.8|2.8|0.4|0.4|3.6|36.0|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.6|0.2|0.2|0.2|0.6|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|265951|98.8|0.6|0.6|0.3|1.5|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|281530|99.6|0.2|0.2|0.2|0.6|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|272758|98.9|0.5|0.5|0.4|1.4|36.0|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|98.2|1.3|0.5|0.4|2.2|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|63110|96.0|2.8|1.2|0.6|4.6|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|65818|98.1|1.3|0.6|0.4|2.3|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|65101|96.0|2.7|1.3|0.6|4.6|36.0|
```


### Training config


See full config in [`config.yaml`](./exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp/config.yaml)


```yaml
config: conf/tuning/train_asr_conformer7_hubert_960hr_large.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp
ngpu: 3
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 3
local_rank: 3
dist_master_addr: localhost
dist_master_port: 33643
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```


model name: xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch


model author: espnet


task: automatic-speech-recognition


tags: en, dataset:librispeech, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- espnet
- audio
- automatic-speech-recognition
language: jp
datasets:
- csj
license: cc-by-4.0


## ESPnet2 ASR model


### `espnet/kan-bayashi_csj_asr_train_asr_conformer`


This model was trained by Nelson Yalta using csj recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2