FastConformer Hybrid Transducer CTC BPE Advances Georgian ASR

NVIDIA’s
latest
development
in
automatic
speech
recognition
(ASR)
technology,
the
FastConformer
Hybrid
Transducer
CTC
BPE
model,
brings
significant
advancements
to
the
Georgian
language,
according
to
NVIDIA
Technical
Blog.
This
new
ASR
model
addresses
the
unique
challenges
presented
by
underrepresented
languages,
particularly
those
with
limited
data
resources.

Optimizing
Georgian
Language
Data

The
primary
hurdle
in
developing
an
effective
ASR
model
for
Georgian
is
the
scarcity
of
data.
The
Mozilla
Common
Voice
(MCV)
dataset
provides
approximately
116.6
hours
of
validated
data,
including
76.38
hours
of
training
data,
19.82
hours
of
development
data,
and
20.46
hours
of
test
data.
Despite
this,
the
dataset
is
still
considered
small
for
robust
ASR
models,
which
typically
require
at
least
250
hours
of
data.

To
overcome
this
limitation,
unvalidated
data
from
MCV,
amounting
to
63.47
hours,
was
incorporated,
albeit
with
additional
processing
to
ensure
its
quality.
This
preprocessing
step
is
crucial
given
the
Georgian
language’s
unicameral
nature,
which
simplifies
text
normalization
and
potentially
enhances
ASR
performance.

Leveraging
FastConformer
Hybrid
Transducer
CTC
BPE

The
FastConformer
Hybrid
Transducer
CTC
BPE
model
leverages
NVIDIA’s
advanced
technology
to
offer
several
advantages:

• 
  Enhanced
  speed
  performance:
  Optimized
  with
  8x
  depthwise-separable
  convolutional
  downsampling,
  reducing
  computational
  complexity.
• 
  Improved
  accuracy:
  Trained
  with
  joint
  transducer
  and
  CTC
  decoder
  loss
  functions,
  enhancing
  speech
  recognition
  and
  transcription
  accuracy.
• 
  Robustness:
  Multitask
  setup
  increases
  resilience
  to
  input
  data
  variations
  and
  noise.
• 
  Versatility:
  Combines
  Conformer
  blocks
  for
  long-range
  dependency
  capture
  and
  efficient
  operations
  for
  real-time
  applications.

Data
Preparation
and
Training

Data
preparation
involved
processing
and
cleaning
to
ensure
high
quality,
integrating
additional
data
sources,
and
creating
a
custom
tokenizer
for
Georgian.
The
model
training
utilized
the
FastConformer
hybrid
transducer
CTC
BPE
model
with
parameters
fine-tuned
for
optimal
performance.

The
training
process
included:

• Processing
  data
• Adding
  data
• Creating
  a
  tokenizer
• Training
  the
  model
• Combining
  data
• Evaluating
  performance
• Averaging
  checkpoints

Extra
care
was
taken
to
replace
unsupported
characters,
drop
non-Georgian
data,
and
filter
by
the
supported
alphabet
and
character/word
occurrence
rates.
Additionally,
data
from
the
FLEURS
dataset
was
incorporated,
adding
3.20
hours
of
training
data,
0.84
hours
of
development
data,
and
1.89
hours
of
test
data.

Performance
Evaluation

Evaluations
on
various
data
subsets
demonstrated
that
incorporating
additional
unvalidated
data
improved
the
Word
Error
Rate
(WER),
indicating
better
performance.
The
robustness
of
the
models
was
further
highlighted
by
their
performance
on
both
the
Mozilla
Common
Voice
and
Google
FLEURS
datasets.

Figures
1
and
2
illustrate
the
FastConformer
model’s
performance
on
the
MCV
and
FLEURS
test
datasets,
respectively.
The
model,
trained
with
approximately
163
hours
of
data,
showcased
commendable
efficiency
and
robustness,
achieving
lower
WER
and
Character
Error
Rate
(CER)
compared
to
other
models.

Comparison
with
Other
Models

Notably,
FastConformer
and
its
streaming
variant
outperformed
MetaAI’s
Seamless
and
Whisper
Large
V3
models
across
nearly
all
metrics
on
both
datasets.
This
performance
underscores
FastConformer’s
capability
to
handle
real-time
transcription
with
impressive
accuracy
and
speed.

Conclusion

FastConformer
stands
out
as
a
sophisticated
ASR
model
for
the
Georgian
language,
delivering
significantly
improved
WER
and
CER
compared
to
other
models.
Its
robust
architecture
and
effective
data
preprocessing
make
it
a
reliable
choice
for
real-time
speech
recognition
in
underrepresented
languages.

For
those
working
on
ASR
projects
for
low-resource
languages,
FastConformer
is
a
powerful
tool
to
consider.
Its
exceptional
performance
in
Georgian
ASR
suggests
its
potential
for
excellence
in
other
languages
as
well.

Discover
FastConformer’s
capabilities
and
elevate
your
ASR
solutions
by
integrating
this
cutting-edge
model
into
your
projects.
Share
your
experiences
and
results
in
the
comments
to
contribute
to
the
advancement
of
ASR
technology.

For
further
details,
refer
to
the
official
source
on

NVIDIA
Technical
Blog.

Image
source:
Shutterstock