MGB2

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+# Ignore Mac system files
+.DS_store
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+# Ignore node_modules folder
+node_modules
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+# ignore .nfs
+
+.nfs*
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+# Ignore all text files
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+.sass-cache
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 *.param
 *.bin

egs/mgb2/ASR/README.md

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+# MGB2
+
+The Multi-Dialect Broadcast News Arabic Speech Recognition (MGB-2):
+The second edition of the Multi-Genre Broadcast (MGB-2) Challenge is
+an evaluation of speech recognition and lightly supervised alignment
+using TV recordings in Arabic. The speech data is broad and multi-genre,
+spanning the whole range of TV output, and represents a challenging task for
+speech technology. In 2016, the challenge featured two new Arabic tracks based
+on TV data from Aljazeera. It was an official challenge at the 2016 IEEE
+Workshop on Spoken Language Technology. The 1,200 hours MGB-2: from Aljazeera
+TV programs have been manually captioned with no timing information.
+QCRI Arabic ASR system has been used to recognize all programs. The ASR output
+was used to align the manual captioning and produce speech segments for
+training speech recognition. More than 20 hours from 2015 programs have been
+transcribed verbatim and manually segmented. This data is split into a
+development set of 10 hours, and a similar evaluation set of 10 hours.
+Both the development and evaluation data have been released in the 2016 MGB
+challenge
+
+Official reference:
+
+Ali, Ahmed, et al. "The MGB-2 challenge: Arabic multi-dialect broadcast media recognition." 
+2016 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2016.
+
+IEEE link: https://ieeexplore.ieee.org/abstract/document/7846277
+
+## Stateless Pruned Transducer Performance Record (after 30 epochs)
+
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+|          greedy search             | 15.52      | 15.28      | --epoch 18, --avg 5, --max-duration 200  |
+| modified beam search               | 13.88      | 13.7       | --epoch 18, --avg 5, --max-duration 200  |
+| fast beam search                   | 14.62      | 14.36      | --epoch 18, --avg 5, --max-duration 200  |
+
+## Conformer-CTC Performance Record (after 40 epochs)
+
+| Decoding method           | dev WER    | test WER |
+|---------------------------|------------|---------|
+| attention-decoder         | 15.62      |  15.01  |
+| whole-lattice-rescoring   | 15.89      |  15.08  |
+
+
+See [RESULTS](/egs/mgb2/ASR/RESULTS.md) for details.

egs/mgb2/ASR/RESULTS.md

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+# Results
+
+
+### MGB2 all data BPE training results (Stateless Pruned Transducer)
+
+#### 2022-09-07
+
+The WERs are
+
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+|          greedy search             | 15.52      | 15.28      | --epoch 18, --avg 5, --max-duration 200 |
+| modified beam search               | 13.88      | 13.7       | --epoch 18, --avg 5, --max-duration 200 |
+| fast beam search                   | 14.62      | 14.36      | --epoch 18, --avg 5, --max-duration 200|
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+
+
+./pruned_transducer_stateless5/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 1 \
+  --exp-dir pruned_transducer_stateless5/exp \
+  --max-duration 300 \
+  --num-buckets 50
+```
+
+The tensorboard training log can be found at
+https://tensorboard.dev/experiment/YyNv45pfQ0GqWzZ898WOlw/#scalars
+
+The decoding command is:
+```
+epoch=18
+avg=5
+for method in greedy_search modified_beam_search fast_beam_search; do
+  ./pruned_transducer_stateless5/decode.py \
+    --epoch $epoch \
+	--beam-size 10 \
+    --avg $avg \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --max-duration 200 \
+    --decoding-method $method \
+    --max-sym-per-frame 1 \
+    --num-encoder-layers 12 \
+    --dim-feedforward 2048 \
+    --nhead 8 \
+    --encoder-dim 512 \
+    --decoder-dim 512 \
+    --joiner-dim 512 \
+    --use-averaged-model True
+done
+```
+
+### MGB2 all data BPE training results (Conformer-CTC) (after 40 epochs)
+
+#### 2022-06-04
+
+You can find a pretrained model, training logs, decoding logs, and decoding results at:
+https://huggingface.co/AmirHussein/icefall-asr-mgb2-conformer_ctc-2022-27-06
+
+The best WER, as of 2022-06-04, for the MGB2 test dataset is below
+
+Using whole lattice HLG decoding + n-gram LM rescoring 
+
+|     | dev        | test       |
+|-----|------------|------------|
+| WER | 15.62      |  15.01     |
+
+Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
+| ngram_lm_scale | attention_scale |
+|----------------|-----------------|
+| 0.1            | -            |
+
+
+Using n-best (n=0.5) attention decoder rescoring
+
+|     | dev        | test       |
+|-----|------------|------------|
+| WER |    15.89   |  15.08     |
+
+Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
+| ngram_lm_scale | attention_scale |
+|----------------|-----------------|
+| 0.01           | 0.5             |
+
+
+To reproduce the above result, use the following commands for training:
+
+# Note: the model was trained on V-100 32GB GPU
+
+```
+cd egs/mgb2/ASR
+. ./path.sh
+./prepare.sh
+export CUDA_VISIBLE_DEVICES="0,1"
+./conformer_ctc/train.py \
+  --lang-dir data/lang_bpe_5000 \
+  --att-rate 0.8 \
+  --lr-factor 10 \
+  --max-duration  \
+  --concatenate-cuts 0 \
+  --world-size 2 \
+  --bucketing-sampler 1 \
+  --max-duration 100 \ 
+  --start-epoch 0 \
+  --num-epochs 40
+
+```
+
+and the following command for nbest decoding
+
+```
+./conformer_ctc/decode.py \
+  --lang-dir data/lang_bpe_5000 \
+  --max-duration 30 \
+  --concatenate-cuts 0 \
+  --bucketing-sampler 1 \
+  --num-paths 1000 \
+  --epoch 40 \
+  --avg 5 \
+  --method attention-decoder \
+  --nbest-scale 0.5
+```
+
+and the following command for whole-lattice decoding
+
+```
+./conformer_ctc/decode.py \
+  --epoch 40 \
+  --avg 5 \
+  --exp-dir conformer_ctc/exp_5000_att0.8 \
+  --lang-dir data/lang_bpe_5000 \
+  --max-duration 30 \
+  --concatenate-cuts 0 \
+  --bucketing-sampler 1 \
+  --num-paths 1000 \
+  --method  whole-lattice-rescoring
+```
+
+
+The tensorboard log for training is available at
+https://tensorboard.dev/experiment/QYNzOi52RwOX8yvtpl3hMw/#scalars
+
+
+### MGB2 100h BPE training results (Conformer-CTC) (after 33 epochs)
+
+#### 2022-06-04
+
+The best WER, as of 2022-06-04, for the MGB2 test dataset is below
+
+Using whole lattice HLG decoding + n-gram LM rescoring 
+
+|     | dev        | test       |
+|-----|------------|------------|
+| WER | 25.32      |  23.53     |
+
+Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
+| ngram_lm_scale | attention_scale |
+|----------------|-----------------|
+| 0.1            | -            |
+
+
+Using n-best (n=0.5) HLG decoding + n-gram LM rescoring + attention decoder rescoring:
+
+|     | dev        | test       |
+|-----|------------|------------|
+| WER |    27.87   |  26.12     |
+
+Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
+| ngram_lm_scale | attention_scale |
+|----------------|-----------------|
+| 0.01           | 0.3             |
+
+
+To reproduce the above result, use the following commands for training:
+
+# Note: the model was trained on V-100 32GB GPU
+
+```
+cd egs/mgb2/ASR
+. ./path.sh
+./prepare.sh
+export CUDA_VISIBLE_DEVICES="0,1"
+./conformer_ctc/train.py \
+  --lang-dir data/lang_bpe_5000 \
+  --att-rate 0.8 \
+  --lr-factor 10 \
+  --max-duration  \
+  --concatenate-cuts 0 \
+  --world-size 2 \
+  --bucketing-sampler 1 \
+  --max-duration 100 \ 
+  --start-epoch 0 \
+  --num-epochs 40
+
+```
+
+and the following command for nbest decoding
+
+```
+./conformer_ctc/decode.py \
+  --lang-dir data/lang_bpe_5000 \
+  --max-duration 30 \
+  --concatenate-cuts 0 \
+  --bucketing-sampler 1 \
+  --num-paths 1000 \
+  --epoch 40 \
+  --avg 5 \
+  --method attention-decoder \
+  --nbest-scale 0.5
+```
+
+and the following command for whole-lattice decoding
+
+```
+./conformer_ctc/decode.py \
+  --lang-dir data/lang_bpe_5000 \
+  --max-duration 30 \
+  --concatenate-cuts 0 \
+  --bucketing-sampler 1 \
+  --num-paths 1000 \
+  --epoch 40 \
+  --avg 5 \
+  --method  whole-lattice-rescoring
+```
+
+The tensorboard log for training is available at
+<https://tensorboard.dev/experiment/zy6FnumCQlmiO7BPsdCmEg/#scalars>
+
+
+
+