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Update GigaSpeech reults (#364)

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* Update decode.py

* Update export.py

* Update results

* Update README.md
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Guanbo Wang 2022-05-15 00:57:40 -04:00 committed by GitHub
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13
README.md
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@ -200,19 +200,22 @@ We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless mod
### GigaSpeech
We provide two models for this recipe: [Conformer CTC model][GigaSpeech_conformer_ctc]
and [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][GigaSpeech_pruned_transducer_stateless2].
#### Conformer CTC
| | Dev | Test |
|-----|-------|-------|
| WER | 10.47 | 10.58 |
#### Pruned stateless RNN-T
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
| | Dev | Test |
|----------------------|-------|-------|
| greedy search | 10.59 | 10.87 |
| fast beam search | 10.56 | 10.80 |
| modified beam search | 10.52 | 10.62 |
| greedy search | 10.51 | 10.73 |
| fast beam search | 10.50 | 10.69 |
| modified beam search | 10.40 | 10.51 |
## Deployment with C++
@ -238,6 +241,8 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
[TIMIT_tdnn_ligru_ctc]: egs/timit/ASR/tdnn_ligru_ctc
[TED-LIUM3_transducer_stateless]: egs/tedlium3/ASR/transducer_stateless
[TED-LIUM3_pruned_transducer_stateless]: egs/tedlium3/ASR/pruned_transducer_stateless
[GigaSpeech_conformer_ctc]: egs/gigaspeech/ASR/conformer_ctc
[GigaSpeech_pruned_transducer_stateless2]: egs/gigaspeech/ASR/pruned_transducer_stateless2
[yesno]: egs/yesno/ASR
[librispeech]: egs/librispeech/ASR
[aishell]: egs/aishell/ASR

2
egs/gigaspeech/ASR/README.md
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@ -16,6 +16,6 @@ ln -sfv /path/to/GigaSpeech download/GigaSpeech
| | Dev | Test |
|--------------------------------|-------|-------|
| `conformer_ctc` | 10.47 | 10.58 |
| `pruned_transducer_stateless2` | 10.52 | 10.62 |
| `pruned_transducer_stateless2` | 10.40 | 10.51 |
See [RESULTS](/egs/gigaspeech/ASR/RESULTS.md) for details.

29
egs/gigaspeech/ASR/RESULTS.md
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@ -11,13 +11,15 @@ decoder contains only an embedding layer, a Conv1d (with kernel
size 2) and a linear layer (to transform tensor dim). k2 pruned
RNN-T loss is used.
The best WER, as of 2022-05-12, for the gigaspeech is below
Results are:
| | Dev | Test |
|----------------------|-------|-------|
| greedy search | 10.59 | 10.87 |
| fast beam search | 10.56 | 10.80 |
| modified beam search | 10.52 | 10.62 |
| greedy search | 10.51 | 10.73 |
| fast beam search | 10.50 | 10.69 |
| modified beam search | 10.40 | 10.51 |
To reproduce the above result, use the following commands for training:
@ -39,33 +41,30 @@ and the following commands for decoding:
```bash
# greedy search
./pruned_transducer_stateless2/decode.py \
--epoch 29 \
--avg 11 \
--iter 3488000 \
--avg 20 \
--decoding-method greedy_search \
--exp-dir pruned_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--max-duration 20 \
--num-workers 1
--max-duration 600
# fast beam search
./pruned_transducer_stateless2/decode.py \
--epoch 29 \
--avg 9 \
--iter 3488000 \
--avg 20 \
--decoding-method fast_beam_search \
--exp-dir pruned_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--max-duration 20 \
--num-workers 1
--max-duration 600
# modified beam search
./pruned_transducer_stateless2/decode.py \
--epoch 29 \
--avg 8 \
--iter 3488000 \
--avg 15 \
--decoding-method modified_beam_search \
--exp-dir pruned_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--max-duration 20 \
--num-workers 1
--max-duration 600
```
Pretrained model is available at

64
egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py
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@ -22,7 +22,7 @@ Usage:
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--max-duration 100 \
--max-duration 600 \
--decoding-method greedy_search
(2) beam search
@ -30,7 +30,7 @@ Usage:
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--max-duration 100 \
--max-duration 600 \
--decoding-method beam_search \
--beam-size 4
@ -39,7 +39,7 @@ Usage:
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--max-duration 100 \
--max-duration 600 \
--decoding-method modified_beam_search \
--beam-size 4
@ -48,7 +48,7 @@ Usage:
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--max-duration 1500 \
--max-duration 600 \
--decoding-method fast_beam_search \
--beam 4 \
--max-contexts 4 \
@ -99,27 +99,28 @@ def get_parser():
"--epoch",
type=int,
default=29,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
help="""It specifies the checkpoint to use for decoding.
Note: Epoch counts from 0.
You can specify --avg to use more checkpoints for model averaging.""",
)
parser.add_argument(
"--iter",
type=int,
default=0,
help="""If positive, --epoch is ignored and it
will use the checkpoint exp_dir/checkpoint-iter.pt.
You can specify --avg to use more checkpoints for model averaging.
""",
)
parser.add_argument(
"--avg",
type=int,
default=8,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
)
parser.add_argument(
"--avg-last-n",
type=int,
default=0,
help="""If positive, --epoch and --avg are ignored and it
will use the last n checkpoints exp_dir/checkpoint-xxx.pt
where xxx is the number of processed batches while
saving that checkpoint.
""",
"'--epoch' and '--iter'",
)
parser.add_argument(
@ -152,7 +153,7 @@ def get_parser():
"--beam-size",
type=int,
default=4,
help="""An interger indicating how many candidates we will keep for each
help="""An integer indicating how many candidates we will keep for each
frame. Used only when --decoding-method is beam_search or
modified_beam_search.""",
)
@ -465,7 +466,11 @@ def main():
)
params.res_dir = params.exp_dir / params.decoding_method
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
if params.iter > 0:
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
else:
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
if "fast_beam_search" in params.decoding_method:
params.suffix += f"-beam-{params.beam}"
params.suffix += f"-max-contexts-{params.max_contexts}"
@ -488,8 +493,9 @@ def main():
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> is defined in local/train_bpe_model.py
# <blk> and <unk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
logging.info(params)
@ -497,8 +503,20 @@ def main():
logging.info("About to create model")
model = get_transducer_model(params)
if params.avg_last_n > 0:
filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n]
if params.iter > 0:
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))

42
egs/gigaspeech/ASR/pruned_transducer_stateless2/export.py
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@ -51,7 +51,11 @@ import sentencepiece as spm
import torch
from train import get_params, get_transducer_model
from icefall.checkpoint import average_checkpoints, load_checkpoint
from icefall.checkpoint import (
average_checkpoints,
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
@ -64,8 +68,19 @@ def get_parser():
"--epoch",
type=int,
default=28,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
help="""It specifies the checkpoint to use for averaging.
Note: Epoch counts from 0.
You can specify --avg to use more checkpoints for model averaging.""",
)
parser.add_argument(
"--iter",
type=int,
default=0,
help="""If positive, --epoch is ignored and it
will use the checkpoint exp_dir/checkpoint-iter.pt.
You can specify --avg to use more checkpoints for model averaging.
""",
)
parser.add_argument(
@ -74,7 +89,7 @@ def get_parser():
default=15,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
"'--epoch' and '--iter'",
)
parser.add_argument(
@ -141,7 +156,24 @@ def main():
model.to(device)
if params.avg == 1:
if params.iter > 0:
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
elif params.avg == 1:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
else:
start = params.epoch - params.avg + 1