Push model using huggingface_hub.

29fcf21b9e6e4fb5b1ba0bb1efd4197e_vocab.txt

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977d4e24975b431ebb44f2dfcdea8778_tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9b04033136c5d0413047fe94d2f0ab6cb088d292014bb076ee1700bdac545a9
+size 260411

README.md

@@ -0,0 +1,85 @@
+---
+{}
+---
+
+## Model Overview
+
+<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>
+
+## NVIDIA NeMo: Training
+
+To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
+```
+pip install nemo_toolkit['all']
+```
+
+## How to Use this Model
+
+The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+
+### Automatically instantiate the model
+
+```python
+import nemo.collections.asr as nemo_asr
+asr_model = nemo_asr.models.ASRModel.from_pretrained("phtran/stt_en_conformer_ctc_small")
+```
+
+### Transcribing using Python
+First, let's get a sample
+```
+wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
+```
+Then simply do:
+```
+asr_model.transcribe(['2086-149220-0033.wav'])
+```
+
+### Transcribing many audio files
+
+```shell
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="phtran/stt_en_conformer_ctc_small"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
+```
+
+### Input
+
+This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
+
+### Output
+
+This model provides transcribed speech as a string for a given audio sample.
+
+## Model Architecture
+
+<ADD SOME INFORMATION ABOUT THE ARCHITECTURE>
+
+## Training
+
+<ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>
+
+### Datasets
+
+<LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>
+
+## Performance
+
+<LIST THE SCORES OF THE MODEL -
+      OR
+USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>
+
+## Limitations
+
+<DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>
+
+Eg:
+Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
+
+
+## References
+
+<ADD ANY REFERENCES HERE AS NEEDED>
+
+[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+
+
+## Original Model Name: ABC
+## Repo ID: nvidia/ABC_XYZ

cf241f7e4d904eaea46bb96f21dd0b1d_tokenizer.vocab

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+▁therefore	-9.06474
+▁police	-9.06516
+▁complete	-9.06974
+▁john	-9.07328
+▁daughter	-9.08041
+▁affect	-9.08141
+▁perhaps	-9.08236
+▁international	-9.08363
+▁themselves	-9.08495
+▁improve	-9.08864
+▁condition	-9.08867
+▁hotel	-9.09553
+▁deliver	-9.09771
+▁sense	-9.09928
+▁relation	-9.10001
+▁sorry	-9.10303
+▁credit	-9.10319
+▁effort	-9.11405
+▁instead	-9.11694
+▁york	-9.12056
+▁united	-9.1221
+▁partner	-9.12363
+▁spoke	-9.12654
+▁strange	-9.12692
+▁everybody	-9.15216
+▁horse	-9.15579
+▁depend	-9.16273
+▁subject	-9.16932
+▁project	-9.17118
+▁approach	-9.17183
+▁involve	-9.1723
+▁listen	-9.17885
+▁draw	-9.17972
+▁computer	-9.18042
+▁married	-9.18112
+▁record	-9.18132
+▁happy	-9.18541
+▁sudden	-9.18597
+▁represent	-9.18688
+▁somebody	-9.1921
+▁correct	-9.19762
+▁serious	-9.19998
+▁decision	-9.20599
+▁society	-9.21113
+▁including	-9.2118
+▁college	-9.21197
+▁english	-9.21531
+▁attack	-9.21725
+▁perform	-9.2217
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+▁accept	-9.23292
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+q	-9.69201

model_config.yaml

@@ -0,0 +1,1209 @@
+sample_rate: 16000
+log_prediction: true
+ctc_reduction: mean_batch
+train_ds:
+  manifest_filepath: /data/NeMo_ASR_SET/English/v2.0/train/tarred_audio_manifest.json
+  sample_rate: 16000
+  batch_size: 64
+  shuffle: true
+  num_workers: 8
+  pin_memory: true
+  use_start_end_token: false
+  trim_silence: false
+  max_duration: 20.0
+  min_duration: 0.1
+  shuffle_n: 2048
+  is_tarred: true
+  tarred_audio_filepaths: /data/NeMo_ASR_SET/English/v2.0/train/audio__OP_0..4095_CL_.tar
+validation_ds:
+  manifest_filepath:
+  - /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-other.json
+  - /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-clean.json
+  - /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-other.json
+  - /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-clean.json
+  sample_rate: 16000
+  batch_size: 64
+  shuffle: false
+  num_workers: 8
+  pin_memory: true
+  use_start_end_token: false
+  is_tarred: false
+  tarred_audio_filepaths: na
+test_ds:
+  manifest_filepath:
+  - /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-other.json
+  - /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-clean.json
+  - /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-other.json
+  - /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-clean.json
+  sample_rate: 16000
+  batch_size: 64
+  shuffle: false
+  num_workers: 8
+  pin_memory: true
+  use_start_end_token: false
+  is_tarred: false
+  tarred_audio_filepaths: na
+tokenizer:
+  dir: /tokenizers/NeMo_ASR_SET/English/asr_set_2.0/tokenizer_spe_unigram_v1024/
+  type: bpe
+  model_path: nemo:977d4e24975b431ebb44f2dfcdea8778_tokenizer.model
+  vocab_path: nemo:29fcf21b9e6e4fb5b1ba0bb1efd4197e_vocab.txt
+  spe_tokenizer_vocab: nemo:cf241f7e4d904eaea46bb96f21dd0b1d_tokenizer.vocab
+preprocessor:
+  _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
+  sample_rate: 16000
+  normalize: per_feature
+  window_size: 0.025
+  window_stride: 0.01
+  window: hann
+  features: 80
+  n_fft: 512
+  log: true
+  frame_splicing: 1
+  dither: 1.0e-05
+  pad_to: 0
+  pad_value: 0.0
+spec_augment:
+  _target_: nemo.collections.asr.modules.SpectrogramAugmentation
+  freq_masks: 2
+  time_masks: 5
+  freq_width: 27
+  time_width: 0.05
+encoder:
+  _target_: nemo.collections.asr.modules.ConformerEncoder
+  feat_in: 80
+  feat_out: -1
+  n_layers: 16
+  d_model: 176
+  subsampling: striding
+  subsampling_factor: 4
+  subsampling_conv_channels: 176
+  ff_expansion_factor: 4
+  self_attention_model: rel_pos
+  n_heads: 4
+  att_context_size:
+  - -1
+  - -1
+  xscaling: true
+  untie_biases: true
+  pos_emb_max_len: 5000
+  conv_kernel_size: 31
+  dropout: 0.1
+  dropout_emb: 0.0
+  dropout_att: 0.1
+decoder:
+  _target_: nemo.collections.asr.modules.ConvASRDecoder
+  feat_in: 176
+  num_classes: 1024
+  vocabulary:
+  - <unk>
+  - s
+  - ▁the
+  - ▁a
+  - t
+  - ▁to
+  - ▁and
+  - ▁i
+  - ▁of
+  - ''''
+  - ed
+  - ▁in
+  - d
+  - ing
+  - 'n'
+  - e
+  - ▁it
+  - ▁that
+  - ▁you
+  - 'y'
+  - er
+  - r
+  - ▁for
+  - m
+  - ▁is
+  - ▁he
+  - re
+  - ▁was
+  - ▁be
+  - p
+  - ly
+  - ▁so
+  - ▁we
+  - a
+  - g
+  - o
+  - ▁c
+  - b
+  - u
+  - ▁on
+  - ▁have
+  - ▁but
+  - ll
+  - ▁with
+  - ▁re
+  - or
+  - ▁s
+  - al
+  - ▁do
+  - ▁know
+  - ar
+  - ▁they
+  - ▁not
+  - ▁as
+  - ▁this
+  - in
+  - le
+  - ▁e
+  - ▁are
+  - ▁like
+  - c
+  - ▁uh
+  - ri
+  - ▁me
+  - ▁his
+  - ▁at
+  - l
+  - es
+  - ▁de
+  - ▁yeah
+  - ▁can
+  - k
+  - ▁or
+  - ▁my
+  - ▁all
+  - ▁had
+  - ▁there
+  - ▁will
+  - ▁one
+  - il
+  - ▁no
+  - ▁what
+  - en
+  - ck
+  - ▁b
+  - ▁f
+  - ce
+  - ch
+  - i
+  - ▁by
+  - ▁she
+  - ▁from
+  - ▁an
+  - ic
+  - ur
+  - ve
+  - w
+  - ter
+  - la
+  - ▁if
+  - ▁just
+  - th
+  - li
+  - ▁
+  - ▁her
+  - ▁um
+  - 'on'
+  - ation
+  - ▁w
+  - ▁would
+  - f
+  - te
+  - ▁st
+  - ▁go
+  - ir
+  - it
+  - ▁out
+  - ro
+  - ▁pa
+  - ▁were
+  - ▁g
+  - ▁t
+  - ion
+  - ▁think
+  - an
+  - ▁right
+  - ▁about
+  - se
+  - lo
+  - ent
+  - ▁up
+  - ment
+  - ate
+  - ▁when
+  - h
+  - ne
+  - ▁don
+  - ▁has
+  - ▁also
+  - ▁more
+  - ▁see
+  - ▁okay
+  - ▁their
+  - ▁your
+  - ge
+  - ▁who
+  - ▁well
+  - ▁co
+  - ▁which
+  - ▁some
+  - ▁se
+  - ▁time
+  - ▁ba
+  - ▁said
+  - ▁con
+  - ers
+  - ▁ra
+  - us
+  - de
+  - ra
+  - ▁him
+  - ▁our
+  - ▁been
+  - ▁fa
+  - ▁po
+  - ▁pro
+  - et
+  - x
+  - ▁la
+  - id
+  - ver
+  - ▁oh
+  - ▁ma
+  - v
+  - ▁now
+  - age
+  - ▁two
+  - ld
+  - ▁mo
+  - ▁how
+  - tion
+  - ▁people
+  - ive
+  - ▁other
+  - ng
+  - ity
+  - z
+  - ist
+  - ▁very
+  - ▁get
+  - ▁any
+  - ▁un
+  - ▁ro
+  - is
+  - ▁work
+  - ▁mean
+  - ▁them
+  - ▁lo
+  - vi
+  - ▁because
+  - ies
+  - ul
+  - as
+  - ad
+  - mp
+  - ▁bo
+  - '-'
+  - ▁then
+  - ▁good
+  - el
+  - nd
+  - ▁li
+  - ▁man
+  - ▁dis
+  - ▁could
+  - ▁ho
+  - at
+  - ol
+  - ▁bu
+  - ▁te
+  - ▁ha
+  - est
+  - me
+  - ▁say
+  - ru
+  - ke
+  - ▁sp
+  - ▁k
+  - able
+  - ▁su
+  - ▁sa
+  - ▁di
+  - ▁fi
+  - ance
+  - ▁really
+  - ▁over
+  - ▁even
+  - ry
+  - ▁us
+  - ▁ca
+  - ow
+  - ho
+  - ▁into
+  - ence
+  - mo
+  - ▁mi
+  - one
+  - qu
+  - ut
+  - lu
+  - ▁o
+  - ty
+  - ▁after
+  - ▁want
+  - ▁new
+  - ▁take
+  - ▁p
+  - ▁look
+  - ▁pre
+  - sh
+  - ▁day
+  - ▁should
+  - ▁th
+  - ▁need
+  - ▁cha
+  - co
+  - ▁much
+  - ▁where
+  - ▁d
+  - ant
+  - ▁fe
+  - ▁da
+  - ▁make
+  - om
+  - ▁did
+  - ▁le
+  - un
+  - ▁only
+  - im
+  - ▁these
+  - ff
+  - ti
+  - ish
+  - ▁ex
+  - ted
+  - ▁first
+  - he
+  - ig
+  - ▁vi
+  - ▁ri
+  - ▁en
+  - ▁com
+  - ated
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+  - ma
+  - ▁way
+  - um
+  - ct
+  - end
+  - ight
+  - ▁here
+  - ▁ta
+  - ▁car
+  - ▁part
+  - ▁come
+  - ia
+  - ▁off
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+  - ▁ah
+  - am
+  - ▁tra
+  - ▁yes
+  - ▁back
+  - ture
+  - ful
+  - ▁pri
+  - ction
+  - ine
+  - ▁three
+  - ard
+  - ▁let
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+  - ▁little
+  - ▁down
+  - mb
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+  - ▁dr
+  - ▁mr
+  - ▁going
+  - ▁comp
+  - po
+  - ▁m
+  - ▁sta
+  - ▁gra
+  - day
+  - ▁many
+  - ian
+  - ta
+  - ▁long
+  - ▁pi
+  - ▁too
+  - ▁app
+  - ▁kind
+  - ous
+  - ci
+  - ▁ga
+  - ten
+  - nt
+  - ▁before
+  - ▁may
+  - ▁got
+  - man
+  - tic
+  - ition
+  - cu
+  - ugh
+  - tra
+  - ▁n
+  - ward
+  - ▁give
+  - ▁every
+  - ▁hi
+  - ting
+  - ▁exp
+  - ▁those
+  - ▁hu
+  - ot
+  - ▁something
+  - ▁lot
+  - ▁still
+  - ▁ne
+  - na
+  - ise
+  - pp
+  - ▁most
+  - ▁gu
+  - ▁state
+  - ▁actually
+  - ▁such
+  - ▁bi
+  - ▁never
+  - tain
+  - ▁great
+  - ▁through
+  - ▁al
+  - 'no'
+  - ▁mar
+  - ▁year
+  - ach
+  - les
+  - ▁school
+  - ally
+  - ial
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+  - ▁old
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+  - ary
+  - ▁ar
+  - ▁years
+  - ▁help
+  - ▁per
+  - ving
+  - ical
+  - ther
+  - ▁does
+  - ac
+  - ca
+  - ▁must
+  - di
+  - ▁own
+  - ▁ru
+  - ▁things
+  - ▁hand
+  - ▁thing
+  - ▁high
+  - ▁last
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+  - ▁under
+  - ▁four
+  - ▁place
+  - ations
+  - ▁sure
+  - mi
+  - nce
+  - ▁am
+  - for
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+  - ▁name
+  - ▁five
+  - ound
+  - ▁op
+  - ▁cons
+  - ▁ph
+  - ▁same
+  - row
+  - ven
+  - ph
+  - ite
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+  - j
+  - ▁sha
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+  - ▁call
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+  - ect
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+  - ▁live
+  - port
+  - ▁mhm
+  - ▁house
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+  - side
+  - ▁va
+  - min
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+  - ▁du
+  - ▁ti
+  - ▁six
+  - ▁men
+  - ▁again
+  - ▁thank
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+  - par
+  - ▁home
+  - op
+  - ▁both
+  - ▁why
+  - ▁put
+  - ▁another
+  - nc
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+  - mit
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+  - led
+  - ▁fo
+  - ▁end
+  - ▁member
+  - ative
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+  - iv
+  - our
+  - red
+  - ▁went
+  - lic
+  - ▁find
+  - ▁pu
+  - land
+  - ▁start
+  - ▁far
+  - ▁eu
+  - ▁imp
+  - ▁always
+  - ▁ju
+  - ▁wa
+  - ▁person
+  - ▁singapore
+  - ap
+  - ▁show
+  - ▁chi
+  - ▁ten
+  - ▁eight
+  - ▁while
+  - ▁point
+  - ▁y
+  - ▁ja
+  - ▁ya
+  - ling
+  - ctor
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+  - nk
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+  - and
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+  - ▁dec
+  - cent
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+  - ▁better
+  - ▁fun
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+  - ship
+  - ton
+  - ▁tell
+  - ▁twenty
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+  - ▁exc
+  - ▁miss
+  - if
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+  - ▁hundred
+  - gg
+  - ▁add
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+  - ities
+  - ▁na
+  - ▁pass
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+  - ▁v
+  - ▁feel
+  - ▁week
+  - ▁win
+  - ible
+  - ▁try
+  - ▁upon
+  - ba
+  - ▁interest
+  - ▁inter
+  - son
+  - line
+  - ▁ob
+  - ▁boy
+  - ▁big
+  - ▁used
+  - ▁seven
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+  - ▁family
+  - less
+  - ▁ki
+  - ber
+  - ▁around
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+  - ▁anything
+  - ▁care
+  - ▁young
+  - ▁guess
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+  - ▁course
+  - ▁agree
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+  - ▁conf
+  - ual
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+  - ating
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+  - ▁hard
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+  - duc
+  - gra
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+  - ▁cre
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+  - ▁each
+  - ▁ve
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+  - ▁country
+  - ▁few
+  - ▁once
+  - ▁'
+  - ▁head
+  - ▁free
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+  - ▁maybe
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+  - ▁night
+  - ▁thousand
+  - ▁face
+  - ▁uhhuh
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+  - ▁nine
+  - ▁close
+  - ▁case
+  - ▁che
+  - ▁against
+  - ▁done
+  - ▁ever
+  - ▁law
+  - ▁believe
+  - ▁public
+  - ▁room
+  - ▁sub
+  - ▁order
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+  - ient
+  - ▁el
+  - ▁children
+  - ▁second
+  - ▁bri
+  - ▁business
+  - ▁hope
+  - ▁move
+  - fa
+  - ▁however
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+  - ▁able
+  - ▁word
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+  - ▁fla
+  - ▁stand
+  - ize
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+  - ▁service
+  - ▁nothing
+  - ▁report
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+  - ▁grow
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+  - ▁himself
+  - ip
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+  - ▁president
+  - ▁system
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+  - ▁stuff
+  - ▁food
+  - ▁job
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+  - ▁err
+  - ▁mind
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+  - ator
+  - ▁half
+  - ▁father
+  - com
+  - ▁less
+  - ▁arm
+  - ▁human
+  - ency
+  - ▁matter
+  - ▁group
+  - ▁girl
+  - ▁current
+  - ▁main
+  - ttle
+  - ▁later
+  - ▁learn
+  - ▁strong
+  - ▁sign
+  - ▁check
+  - ▁light
+  - ▁else
+  - ▁true
+  - ▁term
+  - qui
+  - ▁minute
+  - ▁spec
+  - ▁return
+  - ▁answer
+  - ▁reason
+  - ▁count
+  - ▁shall
+  - ▁communi
+  - ▁travel
+  - ▁wait
+  - ▁provide
+  - ▁low
+  - ▁mother
+  - ▁expect
+  - ▁cause
+  - ▁line
+  - ▁general
+  - lf
+  - ▁getting
+  - ▁parliament
+  - ▁bank
+  - ▁company
+  - ▁stop
+  - cause
+  - ▁power
+  - ▁gi
+  - ▁europe
+  - ▁moment
+  - ▁among
+  - ▁walk
+  - ▁allow
+  - ▁idea
+  - ▁office
+  - ▁town
+  - ▁cannot
+  - ▁countries
+  - ▁become
+  - ▁appear
+  - ▁present
+  - ▁bring
+  - ▁least
+  - ▁almost
+  - ▁kids
+  - ▁remember
+  - ▁include
+  - ▁short
+  - ▁sometimes
+  - ▁game
+  - ▁level
+  - ▁exactly
+  - ▁particular
+  - ▁social
+  - ▁land
+  - ▁woman
+  - ▁north
+  - ▁nice
+  - ▁concern
+  - ▁sort
+  - ▁effect
+  - ▁national
+  - ▁several
+  - ▁safe
+  - ▁until
+  - ▁further
+  - ▁cost
+  - ▁wonder
+  - ▁whether
+  - ▁either
+  - ▁future
+  - ▁pra
+  - ▁council
+  - ▁knew
+  - ▁common
+  - ▁south
+  - ▁making
+  - ▁morning
+  - ▁process
+  - ▁situation
+  - ▁white
+  - ▁result
+  - ▁suppose
+  - ▁employ
+  - ▁political
+  - ▁program
+  - ▁along
+  - ▁women
+  - ▁ski
+  - ▁court
+  - ▁please
+  - ▁shi
+  - ▁possible
+  - ▁protect
+  - ▁experience
+  - ▁definitely
+  - ▁require
+  - ▁account
+  - ▁myself
+  - ▁black
+  - ▁example
+  - ▁america
+  - ▁thirty
+  - ▁student
+  - ▁view
+  - ▁product
+  - ▁wife
+  - ▁health
+  - ▁major
+  - ▁difficult
+  - ▁death
+  - ▁visit
+  - ▁across
+  - ▁receive
+  - ▁voice
+  - ▁citizen
+  - ▁regard
+  - ▁author
+  - ▁treat
+  - ▁especially
+  - ▁local
+  - ▁taking
+  - ▁information
+  - ▁seemed
+  - ▁success
+  - ability
+  - ▁break
+  - ▁whatever
+  - ▁security
+  - ▁address
+  - ▁felt
+  - ▁fifty
+  - ▁million
+  - ▁third
+  - ▁usually
+  - ▁gonna
+  - ▁brother
+  - ▁began
+  - ▁period
+  - ▁east
+  - ▁economic
+  - ▁increase
+  - ▁financial
+  - ▁respect
+  - ▁enjoy
+  - ▁christ
+  - ▁education
+  - ▁brought
+  - ▁organ
+  - ▁parents
+  - ▁policy
+  - ▁round
+  - ▁became
+  - ▁region
+  - ▁lady
+  - ▁discuss
+  - ▁single
+  - ▁early
+  - ▁couple
+  - ▁type
+  - ▁itself
+  - ▁serve
+  - ▁measure
+  - ▁husband
+  - ified
+  - ▁music
+  - ▁ground
+  - ▁companies
+  - ▁street
+  - ▁behind
+  - ▁value
+  - ▁therefore
+  - ▁police
+  - ▁complete
+  - ▁john
+  - ▁daughter
+  - ▁affect
+  - ▁perhaps
+  - ▁international
+  - ▁themselves
+  - ▁improve
+  - ▁condition
+  - ▁hotel
+  - ▁deliver
+  - ▁sense
+  - ▁relation
+  - ▁sorry
+  - ▁credit
+  - ▁effort
+  - ▁instead
+  - ▁york
+  - ▁united
+  - ▁partner
+  - ▁spoke
+  - ▁strange
+  - ▁everybody
+  - ▁horse
+  - ▁depend
+  - ▁subject
+  - ▁project
+  - ▁approach
+  - ▁involve
+  - ▁listen
+  - ▁draw
+  - ▁computer
+  - ▁married
+  - ▁record
+  - ▁happy
+  - ▁sudden
+  - ▁represent
+  - ▁somebody
+  - ▁correct
+  - ▁serious
+  - ▁decision
+  - ▁society
+  - ▁including
+  - ▁college
+  - ▁english
+  - ▁attack
+  - ▁perform
+  - ▁cross
+  - ▁accept
+  - ▁control
+  - ▁flow
+  - ▁although
+  - ▁drink
+  - ▁front
+  - ▁wrong
+  - ▁twi
+  - ▁according
+  - ▁slow
+  - ▁peace
+  - ▁amount
+  - ▁object
+  - ▁movie
+  - ▁benefit
+  - ▁yup
+  - ▁challenge
+  - ▁private
+  - ▁church
+  - ▁wood
+  - ▁field
+  - ▁above
+  - ▁ensure
+  - ▁immediate
+  - ▁figure
+  - ▁foreign
+  - ▁available
+  - ▁insurance
+  - ▁proposal
+  - ▁doubt
+  - ▁strength
+  - ▁difference
+  - ▁stood
+  - ▁implement
+  - ▁economy
+  - ▁detail
+  - ▁umhum
+  - ▁restaurant
+  - ▁collect
+  - ▁global
+  - ▁broke
+  - q
+optim:
+  name: adamw
+  lr: 2.0
+  betas:
+  - 0.9
+  - 0.98
+  weight_decay: 0
+  sched:
+    name: NoamAnnealing
+    d_model: 176
+    warmup_steps: 10000
+    warmup_ratio: null
+    min_lr: 1.0e-06
+target: nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE
+nemo_version: 2.0.0
+decoding:
+  strategy: greedy_batch
+  preserve_alignments: null
+  compute_timestamps: null
+  word_seperator: ' '
+  ctc_timestamp_type: all
+  batch_dim_index: 0
+  greedy:
+    preserve_alignments: false
+    compute_timestamps: false
+    preserve_frame_confidence: false
+    confidence_method_cfg:
+      name: entropy
+      entropy_type: tsallis
+      alpha: 0.33
+      entropy_norm: exp
+      temperature: DEPRECATED
+  beam:
+    beam_size: 4
+    search_type: default
+    preserve_alignments: false
+    compute_timestamps: false
+    return_best_hypothesis: true
+    beam_alpha: 1.0
+    beam_beta: 0.0
+    kenlm_path: null
+    flashlight_cfg:
+      lexicon_path: null
+      boost_path: null
+      beam_size_token: 16
+      beam_threshold: 20.0
+      unk_weight: -.inf
+      sil_weight: 0.0
+    pyctcdecode_cfg:
+      beam_prune_logp: -10.0
+      token_min_logp: -5.0
+      prune_history: false
+      hotwords: null
+      hotword_weight: 10.0
+  wfst:
+    beam_size: 4
+    search_type: riva
+    return_best_hypothesis: true
+    preserve_alignments: false
+    compute_timestamps: false
+    decoding_mode: nbest
+    open_vocabulary_decoding: false
+    beam_width: 10.0
+    lm_weight: 1.0
+    device: cuda
+    arpa_lm_path: null
+    wfst_lm_path: null
+    riva_decoding_cfg: {}
+    k2_decoding_cfg:
+      search_beam: 20.0
+      output_beam: 10.0
+      min_active_states: 30
+      max_active_states: 10000
+  confidence_cfg:
+    preserve_frame_confidence: false
+    preserve_token_confidence: false
+    preserve_word_confidence: false
+    exclude_blank: true
+    aggregation: min
+    tdt_include_duration: false
+    method_cfg:
+      name: entropy
+      entropy_type: tsallis
+      alpha: 0.33
+      entropy_norm: exp
+      temperature: DEPRECATED
+  temperature: 1.0

model_weights.ckpt

@@ -0,0 +1,3 @@
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+oid sha256:be934566674bd3b815a8a72f12b92087a42e7a4d7abbf1840bc46e870bed31a3
+size 52969010