Multires hubert

examples/mr_hubert/README.md

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+# MR-HuBERT
+
+## Pre-trained models
+
+### Main models
+Model | Pretraining Data | Model | Paper Reference
+|---|---|---|---
+MR-HuBERT Base (~97M) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/mono_base/mrhubert_mono_base.pt) |  mono\_base
+MR-HuBERT Base (~321M) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/mono_large/mrhubert_mono_large.pt) |  mono\_large
+Multilingual MR-HuBERT Base (~97M) | [Voxpopuli]() 100k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/multi_base/multi_base.pt) | multi\_base 
+Multilingual MR-HuBERT Large (~321M) | [Voxpopuli]() 100k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/multi_large/multi_large.pt) | Not in the paper
+
+
+### Abalation models
+Model | Pretraining Data | Model | Paper Reference 
+|---|---|---|---
+MR-HuBERT Base (2-4-6 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b1-a/b1-a.pt) | (B.1)-a
+MR-HuBERT Base (5-2-5 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b1-b/b1-b.pt) | (B.1)-b
+MR-HuBERT Base (6-4-2 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b1-c/b1-c.pt) | (B.1)-c
+MR-HuBERT Base (3res 3-2-2-2-3 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b2-a/b2-a.pt) | (B.2)-a
+MR-HuBERT Base (3res 2-2-4-2-2 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b2-b/b2-b.pt) | (B.2)-b
+MR-HuBERT Base (3res 2-2-2-2-2 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b2-c/b2-c.pt) | (B.2)-c
+MR-HuBERT Base (Simple sampling) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b3-a/b3-a.pt) | (B.3)-a
+MR-HuBERT Base (Single target) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b4-a/b4-a.pt) | (B.4)-a
+MR-HuBERT Base (Simple Sampling + single target) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b4-b/b4-b.pt) | (B.4)-b
+MR-HuBERT Base (Mono-resolution 20ms) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b5-a/b5-a.pt) | (B.5)-a
+MR-HuBERT Base (3-3-3 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b6-a/b6-a.pt) | (B.6)-a
+MR-HuBERT Base (Mono-resolution 20ms, 3-3-3 lyrs) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b6-b/b6-b.pt) | (B.6)-b
+MR-HuBERT Base (HuBERT 20ms&40ms units) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b7-a/b7-a.pt) | (B.7)-a
+MR-HuBERT Base (Encodec 50Hz unit) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b7-b/b7-b.pt) | (B.7)-b
+MR-HuBERT Base (Encodec 50Hz units and 25Hz units) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b7-c/b7-c.pt) | (B.7)-c
+MR-HuBERT Base (Encodec 50Hz units stream 0&1 ) | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b7-d/b7-d.pt) | (B.7)-d
+MR-HuBERT Large (no audio norm) | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-a.pt) | (B.8)-a
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-b.pt) | (B.8)-b
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-c.pt) | (B.8)-c
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-d.pt) | (B.8)-d
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-e.pt) | (B.8)-e
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-f.pt) | (B.8)-f
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-g.pt) | (B.8)-g
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-h.pt) | (B.8)-h
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-i.pt) | (B.8)-i
+MR-HuBERT Large (check paper )  | [LibriLight](https://github.com/facebookresearch/libri-light) 60k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/b8-a/b8-j.pt) | (B.8)-j 
+Multilingual MR-HuBERT Large (Simple sampling) | [Voxpopuli]() 100k hr | [download](https://dl.fbaipublicfiles.com/mrhubert/multi_large_simple/multi_large_simple.pt) 
+
+## Load a model
+```
+ckpt_path = "/path/to/the/checkpoint.pt"
+models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
+model = models[0]
+```
+
+## Train a new model
+
+### Data preparation
+
+Follow the steps in `./simple_kmeans` to create:
+- `{train,valid}.tsv` waveform list files with length information
+```
+/path/to/your/audio/files
+file1.wav\t160000
+file2.wav\t154600
+...
+filen.wav\t54362
+```
+- `{train,valid}.km` frame-aligned pseudo label files (the order is the same as wavefiles in the tsv file).
+```
+44 44 44 48 48 962 962 962 962 962 962 962 962 967 967 967 967 967 967 967 967 370 852 370 ... 18 18 745 745
+44 44 44 48 48 962 962 962 147 147 147 147 147 147 147 147 147 147 147 147 176 176 271 271 ... 27 27 745 745
+...
+44 44 44 48 962 962 962 962 962 962 377 377 377 77 77 852 696 694 433 578 578 82 740 622 ... 27 27 745 745
+```
+- `dict.km.txt` a dummy dictionary (first column is id, the second is dummy one)
+```
+0 1
+1 1
+2 1
+...
+999 1
+```
+
+The `label_rate` is the same as the feature frame rate used for clustering,
+which is 100Hz for MFCC features and 50Hz for HuBERT features by default.
+
+### Pre-train a MR-HuBERT model
+
+Suppose `{train,valid}.tsv` are saved at `/path/to/data`, `{train,valid}.km`
+are saved at `/path/to/labels`, and the label rate is 100Hz.
+
+To train a base model (12 layer transformer), run:
+```sh
+$ python fairseq_cli/hydra_train.py \
+  --config-dir /path/to/fairseq-py/examples/mr_hubert/config/pretrain \
+  --config-name mrhubert_base_librispeech \
+  task.data=/path/to/data task.label_dir=/path/to/labels \
+  task.labels='["km"]' model.label_rate=100 \
+  task.label_rate_ratios='[1, 2]' \
+```
+
+Please see sample pre-training scripts `train.sh` for an example script.
+
+### Fine-tune a MR-HuBERT model with a CTC loss
+
+Suppose `{train,valid}.tsv` are saved at `/path/to/data`, and their
+corresponding character transcripts `{train,valid}.ltr` are saved at
+`/path/to/trans`. A typical ltr file is with the same order of tsv waveform files as 
+```
+HOW | ARE | YOU
+...
+THANK | YOU
+```
+
+To fine-tune a pre-trained MR-HuBERT model at `/path/to/checkpoint`, run
+```sh
+$ python fairseq_cli/hydra_train.py \
+  --config-dir /path/to/fairseq-py/examples/mr_hubert/config/finetune \
+  --config-name base_10h \
+  task.data=/path/to/data task.label_dir=/path/to/trans \
+  model.w2v_path=/path/to/checkpoint
+```
+
+Please see sample pre-training scripts `finetune.sh` for an example script.
+
+### Decode a MR-HuBERT model
+
+Suppose the `test.tsv` and `test.ltr` are the waveform list and transcripts of
+the split to be decoded, saved at `/path/to/data`, and the fine-tuned model is
+saved at `/path/to/checkpoint`. 
+
+
+We support three decoding modes:
+- Viterbi decoding: greedy decoding without a language model
+- KenLM decoding: decoding with an arpa-format KenLM n-gram language model
+- Fairseq-LM deocding: decoding with a Fairseq neural language model (not fully tested)
+
+
+#### Viterbi decoding
+
+`task.normalize` needs to be consistent with the value used during fine-tuning.
+Decoding results will be saved at
+`/path/to/experiment/directory/decode/viterbi/test`.
+
+```sh
+$ python examples/speech_recognition/new/infer.py \
+  --config-dir /path/to/fairseq-py/examples/mr_hubert/config/decode \
+  --config-name infer \
+  task.data=/path/to/data \
+  task.normalize=[true|false] \
+  decoding.exp_dir=/path/to/experiment/directory \
+  common_eval.path=/path/to/checkpoint
+  dataset.gen_subset=test \
+```
+
+#### KenLM / Fairseq-LM decoding
+
+Suppose the pronunciation lexicon and the n-gram LM are saved at
+`/path/to/lexicon` and `/path/to/arpa`, respectively. Decoding results will be
+saved at `/path/to/experiment/directory/decode/kenlm/test`.
+
+```sh
+$ python examples/speech_recognition/new/infer.py \
+  --config-dir /path/to/fairseq-py/examples/mr_hubert/config/decode \
+  --config-name infer_lm \
+  task.data=/path/to/data \
+  task.normalize=[true|false] \
+  decoding.exp_dir=/path/to/experiment/directory \
+  common_eval.path=/path/to/checkpoint
+  dataset.gen_subset=test \
+  decoding.decoder.lexicon=/path/to/lexicon \
+  decoding.decoder.lmpath=/path/to/arpa
+```
+
+The command above uses the default decoding hyperparameter, which can be found
+in `examples/speech_recognition/hydra/decoder.py`. These parameters can be
+configured from the command line. For example, to search with a beam size of
+500, we can append the command above with `decoding.decoder.beam=500`.
+Important parameters include:
+- decoding.decoder.beam
+- decoding.decoder.beamthreshold
+- decoding.decoder.lmweight
+- decoding.decoder.wordscore
+- decoding.decoder.silweight
+
+To decode with a Fairseq LM, you may check the usage examples in wav2vec2 or hubert examples.
+
+Please see sample pre-training scripts `decode.sh`  for an example script.

examples/mr_hubert/config/decode/infer.yaml

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+# @package _group_
+
+defaults:
+  - model: null
+
+hydra:
+  run:
+    dir: ${common_eval.results_path}/viterbi
+  sweep:
+    dir: ${common_eval.results_path}
+    subdir: viterbi
+
+task:
+  _name: multires_hubert_pretraining
+  single_target: true
+  fine_tuning: true
+  label_rate_ratios: ???
+  data: ???
+  normalize: false
+
+decoding:
+  type: viterbi
+  unique_wer_file: true
+common_eval:
+  results_path: ???
+  path: ???
+  post_process: letter
+dataset:
+  max_tokens: 1100000
+  gen_subset: ???

examples/mr_hubert/config/decode/infer_lm.yaml

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+# @package _group_
+
+defaults:
+  - model: null
+
+hydra:
+  run:
+    dir: ${common_eval.results_path}/beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight}
+  sweep:
+    dir: ${common_eval.results_path}
+    subdir: beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight}
+
+task:
+  _name: multires_hubert_pretraining
+  single_target: true
+  fine_tuning: true
+  data: ???
+  label_rate_ratios: ???
+  normalize: ???
+
+decoding:
+  type: kenlm
+  lexicon: ???
+  lmpath: ???
+  beamthreshold: 100
+  beam: 500
+  lmweight: 1.5
+  wordscore: -1
+  silweight: 0
+  unique_wer_file: true
+common_eval:
+  results_path: ???
+  path: ???
+  post_process: letter
+dataset:
+  max_tokens: 1100000
+  gen_subset: ???

examples/mr_hubert/config/decode/run/submitit_slurm.yaml

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+# @package _global_
+hydra:
+  launcher:
+    cpus_per_task: ${distributed_training.distributed_world_size}
+    gpus_per_node: ${distributed_training.distributed_world_size}
+    tasks_per_node: ${hydra.launcher.gpus_per_node}
+    nodes: 1
+    mem_gb: 200
+    timeout_min: 4320
+    max_num_timeout: 50
+    name: ${hydra.job.config_name}
+    submitit_folder: ${hydra.sweep.dir}/submitit
+
+distributed_training:
+  distributed_world_size: 1
+  distributed_no_spawn: true
+  distributed_port: 29761

examples/mr_hubert/config/decode/run/submitit_slurm_8gpu.yaml

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+# @package _global_
+hydra:
+  launcher:
+    cpus_per_task: ${distributed_training.distributed_world_size}
+    gpus_per_node: ${distributed_training.distributed_world_size}
+    tasks_per_node: ${hydra.launcher.gpus_per_node}
+    nodes: 1
+    mem_gb: 200
+    timeout_min: 4320
+    max_num_timeout: 50
+    name: ${hydra.job.config_name}
+    submitit_folder: ${hydra.sweep.dir}/submitit
+
+distributed_training:
+  distributed_world_size: 8
+  distributed_no_spawn: true
+  distributed_port: 29761

examples/mr_hubert/config/finetune/base_100h.yaml

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+# @package _group_
+
+common:
+  fp16: true
+  log_format: json
+  log_interval: 200
+  tensorboard_logdir: tblog
+  seed: 1337
+
+checkpoint:
+  no_epoch_checkpoints: true
+  best_checkpoint_metric: wer
+
+distributed_training:
+  ddp_backend: c10d
+  find_unused_parameters: true
+  distributed_world_size: 8
+  distributed_port: 29671
+  nprocs_per_node: 8
+
+task:
+  _name: multires_hubert_pretraining
+  data: ???
+  fine_tuning: true
+  label_dir: ???
+  label_rate_ratios: ???
+  normalize: false  # must be consistent with pre-training
+  labels: ["ltr"]
+  single_target: true
+
+dataset:
+  num_workers: 0
+  max_tokens: 3200000
+  validate_after_updates: ${model.freeze_finetune_updates}
+  validate_interval: 5
+  train_subset: train_100h
+  valid_subset: dev_other
+
+criterion:
+  _name: ctc
+  zero_infinity: true
+
+optimization:
+  max_update: 80000
+  lr: [3e-5]
+  sentence_avg: true
+  update_freq: [1]
+
+optimizer:
+  _name: adam
+  adam_betas: (0.9,0.98)
+  adam_eps: 1e-08
+
+lr_scheduler:
+  _name: tri_stage
+  phase_ratio: [0.1, 0.4, 0.5]
+  final_lr_scale: 0.05
+
+model:
+  _name: multires_hubert_ctc
+  multires_hubert_path: ???
+  apply_mask: true
+  mask_selection: static
+  mask_length: 10
+  mask_other: 0
+  mask_prob: 0.75
+  mask_channel_selection: static
+  mask_channel_length: 64
+  mask_channel_other: 0
+  mask_channel_prob: 0.5
+  layerdrop: 0.1
+  dropout: 0.0
+  activation_dropout: 0.1
+  attention_dropout: 0.0
+  feature_grad_mult: 0.0
+  freeze_finetune_updates: 10000
+
+hydra:
+  job:
+    config:
+      override_dirname:
+        kv_sep: '-'
+        item_sep: '__'
+        exclude_keys:
+          - run
+          - task.data
+          - task.label_dir
+          - model.multires_hubert_path
+          - dataset.train_subset
+          - dataset.valid_subset
+          - criterion.wer_kenlm_model
+          - criterion.wer_lexicon
+  run:
+    dir: ???
+  sweep:
+    dir: ???
+    subdir: ${hydra.job.config_name}__${hydra.job.override_dirname}