init

SpeechLM/speechlm/scripts/tune_speechlm_asr/inference_ctc.sh

+#####################################
+# SpeechLM Base model #
+#####################################
+[ $# -lt 2 ] && echo "Usage: $0 <model_path> <data_dir> [gen-set=dev_clean,dev_other,test_clean,test_other]" && exit 1
+[ ${PWD##*/} != SpeechLM ] && echo "Error: dir not match! Switch to SpeechLM/ and run it again!" && exit 1
+
+model_path=$1
+DATA_DIR=$2
+gen_set=$3
+[ -z $gen_set ] && gen_set="dev_clean,dev_other,test_clean,test_other"
+src_dir=${model_path%/*}
+cpt=${model_path##*/}
+cpt=${cpt%.*}
+
+CODE_ROOT=${PWD}
+
+for subset in ${gen_set//,/ }; do
+    results_path=$src_dir/decode_${cpt}_ctc/${subset}
+    [ ! -d $results_path ] && mkdir -p $results_path
+
+    python $CODE_ROOT/speechlm/infer.py \
+    --config-dir $CODE_ROOT/speechlm/config/decode \
+    --config-name infer_viterbi \
+    common.user_dir=$CODE_ROOT/speechlm \
+    \
+    dataset.gen_subset=${subset} \
+    task.data=$DATA_DIR task.label_dir=$DATA_DIR task.normalize=false \
+    common_eval.results_path=${results_path} common_eval.path=${model_path} \
+    \
+    common_eval.quiet=true \
+    &
+done
+wait
+
+### important to know
+# When loading the fine-tuned model for decoding, fairseq also loads the pre-trained model to use its states['model'] to build the model instance.
+# To prevent the error about the w2v_path (if you don't have the pre-trained model at w2v_path), we set common_eval.model_overrides to override 
+# the w2v_path by speechlmp_base_cfg.pt. speechlmp_base_cfg.pt is just a pre-trained model checkpoint without parameters (only contains config).
+# So, if you have trained a model with different model config (e.g. different encoder layers), you should modify the common_eval.model_overrides to your own.
+    # common_eval.model_overrides=\"{\'w2v_path\':\'$CODE_ROOT/speechlm/config/pretrain/speechlmp_base_cfg.pt\'}\" \

SpeechLM/speechlm/scripts/tune_speechlm_asr/inference_ctc_kenlm.sh

+#####################################
+# SpeechLM Base model #
+#####################################
+[ $# -lt 2 ] && echo "Usage: $0 <model_path> <data_dir> [gen-set=dev_clean,dev_other,test_clean,test_other]" && exit 1
+[ ${PWD##*/} != SpeechLM ] && echo "Error: dir not match! Switch to SpeechLM/ and run it again!" && exit 1
+
+model_path=$1
+DATA_DIR=$2
+gen_set=$3
+[ -z $gen_set ] && gen_set="dev_clean,dev_other,test_clean,test_other"
+src_dir=${model_path%/*}
+cpt=${model_path##*/}
+cpt=${cpt%.*}
+
+CODE_ROOT=${PWD}
+path_to_lexicon=${DATA_DIR}/librispeech_lexicon.lst
+path_to_lm=${DATA_DIR}/4-gram.arpa
+[ ! -f $path_to_lexicon ] && echo "Error: $path_to_lexicon not found !" && exit 1
+[ ! -f $path_to_lm ] && echo "Error: $path_to_lm not found !" && exit 1
+
+for subset in ${gen_set//,/ }; do
+    results_path=$src_dir/decode_${cpt}_ctc/${subset}
+    [ ! -d $results_path ] && mkdir -p $results_path
+
+    python $CODE_ROOT/speechlm/infer.py \
+    --config-dir $CODE_ROOT/speechlm/config/decode \
+    --config-name infer_kenlm \
+    common.user_dir=$CODE_ROOT/speechlm \
+    \
+    dataset.gen_subset=${subset} \
+    task.data=$DATA_DIR task.label_dir=$DATA_DIR task.normalize=false \
+    common_eval.results_path=${results_path} common_eval.path=${model_path} \
+    \
+    decoding.lexicon=$path_to_lexicon \
+    decoding.lmpath=$path_to_lm \
+    decoding.beam=1500 \
+    \
+    common_eval.quiet=false \
+    &
+done
+wait
+
+### important to know
+# When loading the fine-tuned model for decoding, fairseq also loads the pre-trained model to use its states['model'] to build the model instance.
+# To prevent the error about the w2v_path (if you don't have the pre-trained model at w2v_path), we set common_eval.model_overrides to override 
+# the w2v_path by speechlmp_base_cfg.pt. speechlmp_base_cfg.pt is just a pre-trained model checkpoint without parameters (only contains config).
+# So, if you have trained a model with different model config (e.g. different encoder layers), you should modify the common_eval.model_overrides to your own.
+    # common_eval.model_overrides=\"{\'w2v_path\':\'$CODE_ROOT/speechlm/config/pretrain/speechlmp_base_cfg.pt\'}\" \

SpeechLM/speechlm/scripts/tune_speechlm_asr/inference_ctc_large.sh

+#####################################
+# SpeechLM Large model #
+#####################################
+[ $# -lt 2 ] && echo "Usage: $0 <model_path> <data_dir> [gen-set=dev_clean,dev_other,test_clean,test_other]" && exit 1
+[ ${PWD##*/} != SpeechLM ] && echo "Error: dir not match! Switch to SpeechLM/ and run it again!" && exit 1
+
+model_path=$1
+DATA_DIR=$2
+gen_set=$3
+[ -z $gen_set ] && gen_set="dev_clean,dev_other,test_clean,test_other"
+src_dir=${model_path%/*}
+cpt=${model_path##*/}
+cpt=${cpt%.*}
+
+CODE_ROOT=${PWD}
+
+for subset in ${gen_set//,/ }; do
+    results_path=$src_dir/decode_${cpt}_ctc/${subset}
+    [ ! -d $results_path ] && mkdir -p $results_path
+
+    python $CODE_ROOT/speechlm/infer.py \
+    --config-dir $CODE_ROOT/speechlm/config/decode \
+    --config-name infer_viterbi \
+    common.user_dir=$CODE_ROOT/speechlm \
+    \
+    dataset.gen_subset=${subset} \
+    task.data=$DATA_DIR task.label_dir=$DATA_DIR task.normalize=true \
+    common_eval.results_path=${results_path} common_eval.path=${model_path} \
+    \
+    common_eval.quiet=true \
+    &
+done
+wait
+
+# model_path=/mnt/default/v-ziqzhang/data/speechulm/finetune_asr/large_speechlmp_32gpu_4accum/ctc200k_from_400k_bz3.6m_lr1e-5/checkpoint_convert.pt
+# data_dir=/home/v-ziqzhang/dataset/LibriSpeech/asr

SpeechLM/speechlm/scripts/tune_speechlm_asr/inference_ctc_large_fsqlm.sh

+#####################################
+# SpeechLM Large model #
+#####################################
+[ $# -lt 2 ] && echo "Usage: $0 <model_path> <data_dir> [gen-set=dev_clean,dev_other,test_clean,test_other]" && exit 1
+[ ${PWD##*/} != SpeechLM ] && echo "Error: dir not match! Switch to SpeechLM/ and run it again!" && exit 1
+
+model_path=$1
+DATA_DIR=$2
+gen_set=$3
+[ -z $gen_set ] && gen_set="dev_clean,dev_other,test_clean,test_other"
+src_dir=${model_path%/*}
+cpt=${model_path##*/}
+cpt=${cpt%.*}
+
+CODE_ROOT=${PWD}
+path_to_lexicon=${DATA_DIR}/librispeech_lexicon.lst
+path_to_lm=${DATA_DIR}/fairseq_word_lm/lm_librispeech_word_transformer.pt
+[ ! -f $path_to_lexicon ] && echo "Error: $path_to_lexicon not found !" && exit 1
+[ ! -f $path_to_lm ] && echo "Error: $path_to_lm not found !" && exit 1
+
+for subset in ${gen_set//,/ }; do
+    results_path=$src_dir/decode_${cpt}_ctc/${subset}
+    [ ! -d $results_path ] && mkdir -p $results_path
+
+    python $CODE_ROOT/speechlm/infer.py \
+    --config-dir $CODE_ROOT/speechlm/config/decode \
+    --config-name infer_fsqlm \
+    common.user_dir=$CODE_ROOT/speechlm \
+    \
+    dataset.gen_subset=${subset} \
+    task.data=$DATA_DIR task.label_dir=$DATA_DIR task.normalize=true \
+    common_eval.results_path=${results_path} common_eval.path=${model_path} \
+    \
+    decoding.lexicon=$path_to_lexicon \
+    decoding.lmpath=$path_to_lm \
+    decoding.lmweight=0.90 \
+    decoding.wordscore=-0.31 \
+    decoding.beam=500 \
+    \
+    common_eval.quiet=false \
+    &
+done
+wait
+
+# model_path=/mnt/default/v-ziqzhang/data/speechulm/finetune_asr/large_speechlmp_32gpu_4accum/ctc200k_from_400k_bz3.6m_lr1e-5/checkpoint_convert.pt
+# data_dir=/home/v-ziqzhang/dataset/LibriSpeech/asr

SpeechLM/speechlm/scripts/tune_speechlm_st/ft_base_covost_enxx.sh

+# ####################################
+# SpeechLM Base model #
+# ####################################
+[ $# -lt 4 ] && echo "Usage: $0 <model_path> <data_dir> <lang> <cpt-tag> [mount=${PWD}] [world_size=8] [update_freq=2]" && exit 0
+[ ${PWD##*/} != SpeechLM ] && echo "Error: dir not match! Switch to SpeechLM/ and run it again!" && exit 1
+
+w2v_path=$1
+DATA_DIR=$2
+lang=$3
+cpt=$4
+mount=$5
+world_size=$6
+update_freq=$7
+[ -z $mount ] && mount=${PWD}
+[ -z $world_size ] && world_size=8
+[ -z $update_freq ] && update_freq=2
+
+CODE_ROOT=${PWD}
+
+exp_name=${w2v_path%/*}
+exp_name=${exp_name##*/}
+MODEL_DIR="$mount/exp/finetune_covost/$exp_name/legacy_en${lang}_from_${cpt}_bz3.2m_lr1e-4"
+[ -d $MODEL_DIR ] || mkdir -p $MODEL_DIR
+
+max_tokens=1600000
+python $CODE_ROOT/fairseq/fairseq_cli/train.py ${DATA_DIR} \
+    --save-dir ${MODEL_DIR} \
+    --user-dir $CODE_ROOT/speechlm \
+    --task speech_to_text \
+    --config-yaml config_base_en${lang}.yaml \
+    --train-subset "train_st_en_${lang}_local" \
+    --valid-subset "dev_st_en_${lang}_local" \
+    --fp16 \
+    --seed 1 \
+    \
+    --ddp-backend no_c10d \
+    --distributed-world-size ${world_size} \
+    --tensorboard-logdir ${MODEL_DIR} \
+    \
+    --criterion label_smoothed_cross_entropy --report-accuracy \
+    --label-smoothing 0.1 \
+    \
+    --optimizer adam \
+    --clip-norm 1.0 \
+    --lr 1e-04 \
+    --lr-scheduler polynomial_decay --warmup-updates 5000 \
+    --max-update 50000 \
+    --total-num-update 50000 \
+    --update-freq ${update_freq} \
+    \
+    --max-tokens ${max_tokens} \
+    --max-sentences 16 \
+    --max-tokens-valid ${max_tokens} \
+    --grouped-shuffling \
+    --max-source-positions ${max_tokens} \
+    --skip-invalid-size-inputs-valid-test \
+    --num-workers 0 \
+    --best-checkpoint-metric "accuracy" \
+    --maximize-best-checkpoint-metric \
+    \
+    --arch "speechlm_st_legacy" \
+    --w2v-path ${w2v_path} \
+    --layerdrop 0.1 \
+    --decoder-layerdrop 0.1 \
+    --activation-dropout 0.0 \
+    --attention-dropout 0.1 \
+    --feature-grad-mult 1.0 \
+    \
+    --apply-mask --mask-prob 0.5 \
+    \
+    --log-format json \
+    --log-interval 100 \
+    --save-interval 1 \
+    --keep-last-epochs 5 \
+    --keep-best-checkpoints 5 \
+    \
+    2>&1 | tee ${MODEL_DIR}/train.log
+
+# model_path=/mnt/default/v-ziqzhang/data/speechulm/exp/base/base_speechlmp_32gpu_1accum/checkpoint_298_400000.pt
+# data_dir=${HOME}/dataset/CommonVoice/v4/en/en-de

SpeechLM/speechlm/scripts/tune_speechlm_st/ft_large_covost_enxx.sh

+# ####################################
+# SpeechLM Large model #
+# ####################################
+[ $# -lt 4 ] && echo "Usage: $0 <model_path> <data_dir> <lang> <cpt-tag> [mount=${PWD}] [world_size=8] [update_freq=4]" && exit 0
+[ ${PWD##*/} != SpeechLM ] && echo "Error: dir not match! Switch to SpeechLM/ and run it again!" && exit 1
+
+w2v_path=$1
+DATA_DIR=$2
+lang=$3
+cpt=$4
+mount=$5
+world_size=$6
+update_freq=$7
+[ -z $mount ] && mount=${PWD}
+[ -z $world_size ] && world_size=8
+[ -z $update_freq ] && update_freq=4
+
+CODE_ROOT=${PWD}
+
+exp_name=${w2v_path%/*}
+exp_name=${exp_name##*/}
+MODEL_DIR="$mount/exp/finetune_covost/$exp_name/legacy_en${lang}_from_${cpt}_bz3.6m_lr1e-4"
+[ -d $MODEL_DIR ] || mkdir -p $MODEL_DIR
+
+max_tokens=900000
+python $CODE_ROOT/fairseq/fairseq_cli/train.py ${DATA_DIR} \
+    --save-dir ${MODEL_DIR} \
+    --user-dir $CODE_ROOT/speechlm \
+    --task speech_to_text \
+    --config-yaml config_large_en${lang}.yaml \
+    --train-subset "train_st_en_${lang}_local" \
+    --valid-subset "dev_st_en_${lang}_local" \
+    --fp16 \
+    --seed 1 \
+    \
+    --ddp-backend no_c10d \
+    --distributed-world-size ${world_size} \
+    --tensorboard-logdir ${MODEL_DIR} \
+    \
+    --criterion label_smoothed_cross_entropy --report-accuracy \
+    --label-smoothing 0.1 \
+    \
+    --optimizer adam \
+    --clip-norm 1.0 \
+    --lr 1e-04 \
+    --lr-scheduler polynomial_decay --warmup-updates 5000 \
+    --max-update 50000 \
+    --total-num-update 50000 \
+    --update-freq ${update_freq} \
+    \
+    --max-tokens ${max_tokens} \
+    --max-sentences 16 \
+    --max-tokens-valid ${max_tokens} \
+    --grouped-shuffling \
+    --max-source-positions ${max_tokens} \
+    --skip-invalid-size-inputs-valid-test \
+    --num-workers 0 \
+    --best-checkpoint-metric "accuracy" \
+    --maximize-best-checkpoint-metric \
+    \
+    --arch "speechlm_st_legacy" \
+    --w2v-path ${w2v_path} --encoder-embed-dim 1024 \
+    --layerdrop 0.1 \
+    --decoder-layerdrop 0.1 \
+    --activation-dropout 0.0 \
+    --attention-dropout 0.1 \
+    --feature-grad-mult 1.0 \
+    \
+    --apply-mask --mask-prob 0.5 \
+    \
+    --log-format json \
+    --log-interval 100 \
+    --save-interval 1 \
+    --keep-last-epochs 5 \
+    --keep-best-checkpoints 5 \
+    \
+    2>&1 | tee ${MODEL_DIR}/train.log
+
+# model_path=/mnt/default/v-ziqzhang/data/speechulm/exp/large/large_speechlmp_32gpu_4accum/checkpoint_31_400000.pt
+# data_dir=${HOME}/dataset/CommonVoice/v4/en/en-de

SpeechLM/speechlm/scripts/tune_speechlm_st/inference_base.sh

+# ####################################
+# SpeechLM Base model #
+# ####################################
+[ $# -lt 3 ] && echo "Usage: $0 <model_path> <data_dir> <lang> [gen-set=dev] [beam_size=5] [lenpen=1.0]" && exit 0
+[ ${PWD##*/} != SpeechLM ] && echo "Error: dir not match! Switch to SpeechLM/ and run it again!" && exit 1
+
+model_path=$1
+DATA_DIR=$2
+lang=$3
+gen_set=$4
+beam_size=$5
+lenpen=$6
+[ -z $gen_set ] && gen_set="dev"
+[ -z $beam_size ] && beam_size=5
+[ -z $lenpen ] && lenpen=1
+src_dir=${model_path%/*}
+cpt=${model_path##*/}
+cpt=${cpt%.*}
+
+CODE_ROOT=${PWD}
+results_path=$src_dir/decode_${cpt}_beam${beam_size}/${gen_set}
+[ ! -d $results_path ] && mkdir -p $results_path
+
+python $CODE_ROOT/fairseq/fairseq_cli/generate.py $DATA_DIR \
+    --gen-subset ${gen_set}_st_en_${lang}_local \
+    --max-tokens 2300000 \
+    --max-source-positions 2300000 \
+    --num-workers 0 \
+    \
+    --user-dir $CODE_ROOT/speechlm \
+    --task speech_to_text \
+    --config-yaml config_base_en${lang}.yaml \
+    \
+    --path ${model_path} \
+    --results-path $results_path \
+    \
+    --scoring sacrebleu --max-len-a 0 --max-len-b 512 \
+    --beam ${beam_size} \
+    --lenpen $lenpen \
+
+    echo $results_path
+    tail -n 1 $results_path/generate-*.txt
+    sleep 1s
+
+# model_path=/mnt/default/v-ziqzhang/data/speechulm/finetune_covost/base_speechlmp_32gpu_1accum/legacy_ende_from_400k_bz3.2m_lr1e-4/checkpoint_best_convert.pt
+# data_dir=dataset/CommonVoice/v4/en/en-de

SpeechLM/speechlm/scripts/tune_speechlm_st/inference_large.sh

+# ####################################
+# SpeechLM Base model #
+# ####################################
+[ $# -lt 3 ] && echo "Usage: $0 <model_path> <data_dir> <lang> [gen-set=dev] [beam_size=5] [lenpen=1.0]" && exit 0
+[ ${PWD##*/} != SpeechLM ] && echo "Error: dir not match! Switch to SpeechLM/ and run it again!" && exit 1
+
+model_path=$1
+DATA_DIR=$2
+lang=$3
+gen_set=$4
+beam_size=$5
+lenpen=$6
+[ -z $gen_set ] && gen_set="dev"
+[ -z $beam_size ] && beam_size=5
+[ -z $lenpen ] && lenpen=1
+src_dir=${model_path%/*}
+cpt=${model_path##*/}
+cpt=${cpt%.*}
+
+CODE_ROOT=${PWD}
+results_path=$src_dir/decode_${cpt}_beam${beam_size}/${gen_set}
+[ ! -d $results_path ] && mkdir -p $results_path
+
+python $CODE_ROOT/fairseq/fairseq_cli/generate.py $DATA_DIR \
+    --gen-subset ${gen_set}_st_en_${lang}_local \
+    --max-tokens 2300000 \
+    --max-source-positions 2300000 \
+    --num-workers 0 \
+    \
+    --user-dir $CODE_ROOT/speechlm \
+    --task speech_to_text \
+    --config-yaml config_large_en${lang}.yaml \
+    \
+    --path ${model_path} \
+    --results-path $results_path \
+    \
+    --scoring sacrebleu --max-len-a 0 --max-len-b 512 \
+    --beam ${beam_size} \
+    --lenpen $lenpen \
+
+    echo $results_path
+    tail -n 1 $results_path/generate-*.txt
+    sleep 1s
+
+# model_path=/mnt/default/v-ziqzhang/data/speechulm/finetune_covost/large_speechlmp_32gpu_4accum/legacy_ende_from_400k_bz3.6m_lr1e-4/checkpoint.avgnbest_convert.pt
+# data_dir=dataset/CommonVoice/v4/en/en-de

SpeechLM/speechlm/tasks/fast_text_to_unit.py

+# ----------------------------------------------------------------------------
+# SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data (https://arxiv.org/abs/2209.15329)
+# Github source: https://github.com/microsoft/SpeechT5/tree/main/SpeechLM
+# Code based on fairseq: https://github.com/facebookresearch/fairseq/tree/272c4c5197250997148fb12c0db6306035f166a4
+# 
+# Copyright (c) 2022 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# ----------------------------------------------------------------------------
+
+import torch
+import numpy as np
+import logging
+from pathlib import Path
+from argparse import Namespace
+
+from fairseq.tasks import LegacyFairseqTask, register_task
+from fairseq.data import Dictionary, encoders
+from fairseq.data.audio.speech_to_text_joint_dataset import S2TJointDataConfig
+
+from speechlm.unit_generator import NonAutoregressiveUnitGenerator
+from speechlm.data.text_to_unit_dataset import Text2UnitDatasetCreator
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+@register_task("fast_text_to_unit")
+class FastTextToUnitTask(LegacyFairseqTask):
+    @staticmethod
+    def add_args(parser):
+        parser.add_argument("data", help="manifest root path")
+        parser.add_argument(
+            "--config-yaml",
+            type=str,
+            default="config.yaml",
+            help="Configuration YAML filename (under manifest root)",
+        )
+        parser.add_argument(
+            "--max-source-positions",
+            default=2048,
+            type=int,
+            metavar="N",
+            help="max number of tokens in the source sequence",
+        )
+        parser.add_argument(
+            "--max-target-positions",
+            default=1024,
+            type=int,
+            metavar="N",
+            help="max number of tokens in the target sequence",
+        )
+        parser.add_argument("--n-frames-per-step", type=int, default=1)
+        parser.add_argument("--eos-prob-threshold", type=float, default=0.5)
+        parser.add_argument("--eval-inference", action="store_true")
+        parser.add_argument("--eval-tb-nsample", type=int, default=8)
+        parser.add_argument("--vocoder", type=str, default="griffin_lim")
+        parser.add_argument("--spec-bwd-max-iter", type=int, default=8)
+
+    def __init__(self, args, src_dict, tgt_dict):
+        super().__init__(args)
+        self.src_dict = src_dict
+        self.tgt_dict = tgt_dict
+        self.data_cfg = S2TJointDataConfig(Path(args.data) / args.config_yaml)
+        self.speaker_to_id = self._get_speaker_to_id()
+        
+    @classmethod
+    def setup_task(cls, args, **kwargs):
+        data_cfg = S2TJointDataConfig(Path(args.data) / args.config_yaml)
+        src_dict_path = Path(args.data) / data_cfg.src_vocab_filename
+        if not src_dict_path.is_file():
+            raise FileNotFoundError(f"Dict not found: {src_dict_path.as_posix()}")
+        src_dict = Dictionary.load(src_dict_path.as_posix())
+        logger.info(
+            f"Source dictionary size ({data_cfg.src_vocab_filename}): " f"{len(src_dict):,}"
+        )
+        tgt_dict_path = Path(args.data) / data_cfg.vocab_filename
+        if not tgt_dict_path.is_file():
+            raise FileNotFoundError(f"Dict not found: {tgt_dict_path.as_posix()}")
+        tgt_dict = Dictionary.load(tgt_dict_path.as_posix())
+        logger.info(
+            f"Target dictionary size ({data_cfg.vocab_filename}): " f"{len(tgt_dict):,}"
+        )
+
+        if getattr(args, "train_subset", None) is not None:
+            if not all(s.startswith("train") for s in args.train_subset.split(",")):
+                raise ValueError('Train splits should be named like "train*".')
+        return cls(args, src_dict, tgt_dict)
+    
+    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
+        is_train_split = split.startswith("train")
+        pre_tokenizer = self.build_tokenizer(self.args)
+        bpe_tokenizer = self.build_bpe(self.args)
+        self.datasets[split] = Text2UnitDatasetCreator.from_tsv(
+            self.args.data,
+            self.data_cfg,
+            split,
+            self.src_dict,
+            pre_tokenizer,
+            bpe_tokenizer,
+            is_train_split=is_train_split,
+            epoch=epoch,
+            seed=self.args.seed,
+            n_frames_per_step=self.args.n_frames_per_step,
+            speaker_to_id=self.speaker_to_id,
+        )
+
+    @property
+    def target_dictionary(self):
+        return self.tgt_dict
+
+    @property
+    def source_dictionary(self):
+        return self.src_dict
+
+    def max_positions(self):
+        return self.args.max_source_positions, self.args.max_target_positions
+
+    def _get_speaker_to_id(self):
+        speaker_to_id = None
+        speaker_set_filename = self.data_cfg.config.get("speaker_set_filename")
+        if speaker_set_filename is not None:
+            speaker_set_path = Path(self.args.data) / speaker_set_filename
+            with open(speaker_set_path) as f:
+                speaker_to_id = {r.strip(): i for i, r in enumerate(f)}
+        return speaker_to_id
+
+    @classmethod
+    def get_speaker_embeddings(cls, args):
+        # It Will be used in FastText2UnitModel model, insdead of nn.Embedding on speaker-id, we default to use x-vectors extracted ahead.
+        # This is for varying the speaker information when generating units from text.
+        if args.speaker_to_id is not None:
+            embed_speaker = torch.nn.Embedding(
+                len(args.speaker_to_id), args.speaker_embed_dim
+            )
+        elif args.speaker_embedding_type == "x-vector":
+            # return LayerNorm(args.speaker_embed_dim)
+            return lambda x: x.unsqueeze(1)
+        elif args.speaker_embedding_type == "i-vector":
+            # return LayerNorm(args.speaker_embed_dim)
+            return lambda x: x
+        else:
+            embed_speaker = None
+        return embed_speaker
+
+    def build_model(self, cfg):
+        cfg.pitch_min = self.data_cfg.config["features"].get("pitch_min", None)
+        cfg.pitch_max = self.data_cfg.config["features"].get("pitch_max", None)
+        cfg.energy_min = self.data_cfg.config["features"].get("energy_min", None)
+        cfg.energy_max = self.data_cfg.config["features"].get("energy_max", None)
+        cfg.speaker_to_id = self.speaker_to_id
+        cfg.speaker_embedding_type = self.data_cfg.config.get("speaker_embedding_type", None)
+        model = super().build_model(cfg)
+        self.generator = None
+        if getattr(cfg, "eval_inference", False):
+            self.generator = self.build_generator([model], cfg)
+        return model
+
+    def build_generator(self, models, cfg, vocoder=None, **unused):
+        model = models[0]
+        assert getattr(model, "NON_AUTOREGRESSIVE") is True
+        return NonAutoregressiveUnitGenerator(model, vocoder, self.data_cfg)
+
+
+    def build_tokenizer(self, args):
+        logger.info(f"pre-tokenizer: {self.data_cfg.pre_tokenizer}")
+        return encoders.build_tokenizer(Namespace(**self.data_cfg.pre_tokenizer))
+
+    def build_bpe(self, args):
+        logger.info(f"tokenizer: {self.data_cfg.bpe_tokenizer}")
+        return encoders.build_bpe(Namespace(**self.data_cfg.bpe_tokenizer))

SpeechLM/speechlm/tasks/joint_sc2t_pretrain.py

+# ----------------------------------------------------------------------------
+# SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data (https://arxiv.org/abs/2209.15329)
+# Github source: https://github.com/microsoft/SpeechT5/tree/main/SpeechLM
+# Code based on fairseq: https://github.com/facebookresearch/fairseq/tree/272c4c5197250997148fb12c0db6306035f166a4
+# 
+# Copyright (c) 2022 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# ----------------------------------------------------------------------------
+
+import logging
+import os
+import sys
+from typing import Dict, List, Optional, Tuple
+from pathlib import Path
+
+import numpy as np
+from argparse import Namespace
+from collections import OrderedDict
+
+import torch
+from dataclasses import dataclass, field
+from fairseq.data import (
+    Dictionary,
+    encoders,
+    data_utils,
+    StripTokenDataset,
+    PrependTokenDataset,
+    AppendTokenDataset,
+    DenoisingDataset,
+    ConcatDataset,
+    FairseqDataset,
+    iterators,
+    ResamplingDataset,
+    MaskTokensDataset,
+    LanguagePairDataset,
+)
+from fairseq.data.audio.speech_to_text_joint_dataset import S2TJointDataConfig
+from fairseq.data.shorten_dataset import maybe_shorten_dataset
+# from fairseq.data.encoders.utils import get_whole_word_mask
+from fairseq.dataclass.configs import FairseqDataclass
+from fairseq.tasks import register_task
+from fairseq.tasks.fairseq_task import FairseqTask
+from fairseq.dataclass.constants import ChoiceEnum
+from omegaconf import MISSING
+
+from speechlm.data.multimodal_corpus_dataset import MultiCorpusDataset
+from speechlm.data.load_langpair_dataset  import load_langpair_dataset
+from speechlm.data.language_trible_dataset import LanguageTripleDataset, load_langtriple_dataset
+from speechlm.data.hubert_dataset import HubertDataset
+
+logger = logging.getLogger(__name__)
+
+TOKENIZER_CHOICES = ChoiceEnum(["sentencepiece", "hubert_letters", "none"])
+
+def _lang_token(lang: str):
+    return "<lang:{}>".format(lang)
+
+def _lang_token_index(dic: Dictionary, lang: str):
+    """Return language token index."""
+    idx = dic.index(_lang_token(lang))
+    assert idx != dic.unk_index, "cannot find language token for lang {}".format(lang)
+    return idx
+
+
+class LabelEncoder(object):
+    def __init__(self, dictionary: Dictionary) -> None:
+        self.dictionary = dictionary
+
+    def __call__(self, label: str) -> List[str]:
+        return self.dictionary.encode_line(
+            label, append_eos=False, add_if_not_exist=False,
+        )
+
+
+### wrap the initial get_whole_word_mask which needs bpe_tokenizer,
+### here we just assume words are splited by "|" or "<SIL>"
+def get_whole_word_mask(args, dictionary):
+    def is_beginning_of_word(i):
+        if i < dictionary.nspecial:
+            # special elements are always considered beginnings
+            return True
+        tok = dictionary[i]
+        if tok.startswith("madeupword"):
+            return True
+        elif tok in ["<unk>", "<s>", "</s>", "<pad>", "|", "<eps>"]:
+            return True
+        else:
+            return False
+
+    mask_whole_words = torch.ByteTensor(
+        list(map(is_beginning_of_word, range(len(dictionary))))
+    )
+    return mask_whole_words
+
+def get_repeative_start(tokens):
+    """
+    tokens: torch.Tensor with repeative tokens
+    """
+    length = len(tokens)
+    rep_start_id = tokens[:-1] != tokens[1:]
+    return torch.cat([torch.tensor([True]), rep_start_id])
+
+@dataclass
+class TextPretrainingConfig(FairseqDataclass):    
+    ### added for joint pretraining
+    text_data: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "if set, path to text data directory",
+        },
+    )
+    seed: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": "for ordered_indices in MulticorpusDataset",
+        },
+    )
+    tokens_per_sample: Optional[int] = field(
+        default=512,
+        metadata={
+            "help": "max number of total tokens over all segments per sample for dataset",
+        },
+    )
+    tokens_per_sample_tgt: Optional[int] = field(
+        default=512,
+        metadata={
+            "help": "max number of total tokens over all segments per target sample for dataset",
+        },
+    )
+    sample_break_mode: Optional[str] = field(
+        default="eos",
+        metadata={
+            "help": "mode for breaking sentence",
+        },
+    )
+    mask: Optional[float] = field(
+        default=0.3,
+        metadata={
+            "help": "fraction of words/subwords that will be masked",
+        },
+    )
+    leave_unmasked_prob: float = field(
+        default=0.1,
+        metadata={"help": "probability that a masked token is unmasked"},
+    )
+    mask_random: Optional[float] = field(
+        default=0.1,
+        metadata={
+            "help": "instead of using [MASK], use random token this often",
+        },
+    )
+    freq_weighted_replacement: bool = field(
+        default=False,
+        metadata={"help": "sample random replacement words based on word frequencies"},
+    )
+    mask_whole_words: bool = field(
+        default=True,
+        metadata={"help": "mask whole words; you may also want to set --bpe"},
+    )
+    mask_repeative_tokens: bool = field(
+        default=True,
+        metadata={"help": "mask repeative_tokens; if mask_whole_words=False"},
+    )
+    mask_multiple_length: int = field(
+        default=1,
+        metadata={"help": "repeat the mask indices multiple times"},
+    )
+    mask_stdev: float = field(
+        default=0.0,
+        metadata={"help": "stdev of the mask length"},
+    )
+    shorten_method: Optional[str] = field(
+        default="none",
+        metadata={
+            "help": "if not none, shorten sequences that exceed tokens_per_sample",
+            "choices": "none/truncate/random_crop"
+        },
+    )
+    shorten_data_split_list: Optional[str] = field(
+        default="",
+        metadata={
+            "help": "comma_separated list of dataset splits to apply shortening to, e.g., train,valid (default: all dataset splits)",
+        },
+    )
+
+    ### below hypra-parameters is used in bart
+    insert: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": "insert this percentage of additional random tokens",
+        },
+    )
+    permute: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": "take this proportion of subwords and permute them",
+        },
+    )
+    rotate: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": "rotate this proportion of inputs",
+        },
+    )
+    poisson_lambda: Optional[float] = field(
+        default=3.5,
+        metadata={
+            "help": "randomly shuffle sentences for this proportion of inputs",
+        },
+    )
+    permute_sentences: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": "shuffle this proportion of sentences in all inputs",
+        },
+    )
+    mask_length: Optional[str] = field(
+        default="span-poisson",
+        metadata={
+            "help": "mask length to choose",
+            "choice": "subword/word/span-poisson"
+        },
+    )
+    replace_length: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": "when masking N tokens, replace with 0, 1, or N tokens (use -1 for N)",
+        },
+    )
+    shuffle_instance: Optional[bool] = field(
+        default=False,
+        metadata={"help": "shuffle instance"},
+    )
+    max_source_positions: Optional[int] = field(
+        default=1024,
+        metadata={"help": "max number of tokens in the source sequence"},
+    )
+    max_target_positions: Optional[int] = field(
+        default=1024,
+        metadata={"help": "max number of tokens in the target sequence"},
+    )
+    bpe: Optional[str] = field(
+        default="",
+        metadata={
+            "help": "will wrapped by the text_data_config yaml",
+        },
+    )
+    data_config: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "a config yaml specify the bpe model of text data",
+        },
+    )
+    text_maxtokens_ratio: Optional[float] = field(
+        default=1.0,
+        metadata={
+            "help": "for text, max_tokens = max_tokens * text_maxtokens_ratio / 320 ",
+        },
+    )
+    prepend_tgt_lang_tag: bool = field(
+        default=False,
+        metadata={"help": "prepend tgt_lang_tag to replace <eos>"},
+    )
+    mask_text_ratio: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": "mask_text_ratio, for paired data",
+        },
+    )
+    truncate_mono_source: bool = field(
+        default=True,
+        metadata={"help": "truncate mono source-side examples that exceed max-positions"},
+    )
+
+
+@dataclass
+class JointPretrainingConfig(FairseqDataclass):
+    data: str = field(
+        default=MISSING, metadata={"help": "path to speech data directory"}
+    )
+    fine_tuning: bool = field(
+        default=False, metadata={"help": "set to true if fine-tuning Hubert"}
+    )
+    labels: List[str] = field(
+        default_factory=lambda: ["ltr"],
+        metadata={
+            "help": (
+                "extension of the label files to load, frame-level labels for"
+                " pre-training, and sequence-level label for fine-tuning"
+            )
+        },
+    )
+    label_dir: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "if set, looks for labels in this directory instead",
+        },
+    )
+    label_rate: int = field(
+        default=-1,
+        metadata={"help": "label frame rate. -1 for sequence label"},
+    )
+    sample_rate: int = field(
+        default=16_000,
+        metadata={
+            "help": "target sample rate. audio files will be up/down "
+            "sampled to this rate"
+        },
+    )
+    normalize: bool = field(
+        default=False,
+        metadata={
+            "help": "if set, normalizes input to have 0 mean and unit variance"
+        },
+    )
+    enable_padding: bool = field(
+        default=False,
+        metadata={"help": "pad shorter samples instead of cropping"},
+    )
+    max_keep_size: Optional[int] = field(
+        default=None,
+        metadata={"help": "exclude sample longer than this"},
+    )
+    max_sample_size: Optional[int] = field(
+        default=None,
+        metadata={"help": "max sample size to crop to for batching"},
+    )
+    min_sample_size: Optional[int] = field(
+        default=None,
+        metadata={"help": "min sample size to crop to for batching"},
+    )
+    single_target: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "if set, AddTargetDatasets outputs same keys "
+            "as AddTargetDataset"
+        },
+    )
+    random_crop: Optional[bool] = field(
+        default=True,
+        metadata={"help": "always crop from the beginning if false"},
+    )
+    pad_audio: Optional[bool] = field(
+        default=False,
+        metadata={"help": "pad audio to the longest one in the batch if true"},
+    )
+    store_labels: Optional[bool] = field(
+        default=True,
+        metadata={"help": "store spm labels in memory, should be true when fine-tune with bpe"},
+    )
+    add_decoder_target: bool = field(
+        default=False,
+        metadata={"help": "contral the model architecture, if set True, load reduced unit as target"},
+    )
+    split_modality_batch: bool = field(
+        default=False,
+        metadata={"help": "whether create all samples of different modalities in a batch"},
+    )
+    speech_tgt_lang: str = field(
+        default="",
+        metadata={"help": "prepend <tgt-id> to prev_output_tokens to replace <eos>, only used for decoder"},
+    )
+    speech_sampling_alpha: float = field(
+        default=0.2,
+        metadata={
+            "help": "Hyper-parameter alpha = 1/T for temperature-based speech resampling."
+            "(alpha = 1 for no resampling)"
+        },
+    )
+    text_sampling_alpha: float = field(
+        default=0.2,
+        metadata={
+            "help": "Hyper-parameter alpha = 1/T for temperature-based text resampling."
+            "(alpha = 1 for no resampling)"
+        },
+    )
+    hubert_tokenizer: Optional[TOKENIZER_CHOICES] = field(
+        default="none",
+        metadata={"help": "which tokenizer for processing text"},
+    )
+    sp_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "sentencepiece model path if using bpe tokenizer"},
+    )
+
+    text_cfg: TextPretrainingConfig = TextPretrainingConfig()
+
+
+@register_task("joint_sc2t_pretraining", dataclass=JointPretrainingConfig)
+class Jsc2tPretrainingTask(FairseqTask):
+
+    cfg: JointPretrainingConfig
+
+    def __init__(
+        self,
+        cfg: JointPretrainingConfig,
+    ) -> None:
+        super().__init__(cfg)
+
+        logger.info(f"current directory is {os.getcwd()}")
+        logger.info(f"JSTPretrainingTask Config {cfg}")
+
+        self.cfg = cfg
+        self.fine_tuning = cfg.fine_tuning
+        self.blank_symbol = "<s>"
+
+        self.state.add_factory("hubert_tokenizer", self.build_tokenizer)
+        if self.cfg.text_cfg.text_data is not None and os.path.exists(self.cfg.text_cfg.text_data):
+            self.state.add_factory("text_dictionary", self.load_text_dictionary)
+            self.state.add_factory("text_src_dictionary", self.load_text_src_dictionary)
+        if cfg.fine_tuning:
+            self.state.add_factory("target_dictionary", self.load_dictionaries)
+        else:
+            self.state.add_factory("dictionaries", self.load_dictionaries)
+
+        if cfg.text_cfg.data_config is not None:
+            self.text_data_cfg = S2TJointDataConfig(Path(f"{cfg.text_cfg.text_data}/{cfg.text_cfg.data_config}"))
+            self.cfg.text_cfg.bpe = self.text_data_cfg.bpe_tokenizer["bpe"]
+        else:
+            self.text_data_cfg = None
+
+    @property
+    def source_dictionary(self) -> Optional[Dictionary]:
+        return None
+
+    @property
+    def target_dictionary(self) -> Optional[Dictionary]:
+        return self.state.target_dictionary
+
+    @property
+    def dictionaries(self) -> List[Dictionary]:
+        return self.state.dictionaries
+
+    @property
+    def text_dictionary(self) -> Optional[Dictionary]:
+        return self.state.text_dictionary
+
+    @property
+    def text_src_dictionary(self) -> Optional[Dictionary]:
+        return self.state.text_src_dictionary
+
+    @property
+    def hubert_tokenizer(self):
+        return self.state.hubert_tokenizer
+
+    def load_dictionaries(self):
+        label_dir = self.cfg.data if self.cfg.label_dir is None else self.cfg.label_dir
+        dictionaries = [Dictionary.load(f"{label_dir}/dict.{label}.txt") for label in self.cfg.labels]
+        if not self.cfg.fine_tuning:
+            for dictionary in dictionaries:
+                dictionary.add_symbol("<mask>")
+        return dictionaries[0] if self.cfg.fine_tuning else dictionaries
+    
+    def load_text_dictionary(self):
+        tgt_dict_path = f"{self.cfg.text_cfg.text_data}/{self.text_data_cfg.vocab_filename if self.text_data_cfg is not None else 'dict.txt'}"
+        if not os.path.isfile(tgt_dict_path):
+            raise FileNotFoundError(f"Dict not found: {tgt_dict_path}")
+        text_dictionary = Dictionary.load(tgt_dict_path)
+        self.mask_idx = text_dictionary.add_symbol("<mask>")
+        return text_dictionary
+
+    def load_text_src_dictionary(self):
+        src_dict_path = f"{self.cfg.text_cfg.text_data}/{self.text_data_cfg.src_vocab_filename if self.text_data_cfg is not None else 'dict.txt'}"
+        if not os.path.isfile(src_dict_path):
+            raise FileNotFoundError(f"Dict not found: {src_dict_path}")
+        src_text_dictionary = Dictionary.load(src_dict_path)
+        self.mask_idx = src_text_dictionary.add_symbol("<mask>")
+        return src_text_dictionary
+
+    @classmethod
+    def setup_task(
+        cls, cfg: JointPretrainingConfig, **kwargs
+    ) -> "Jsc2tPretrainingTask":
+        return cls(cfg)
+
+    def get_label_dir(self) -> str:
+        if self.cfg.label_dir is None:
+            return self.cfg.data
+        return self.cfg.label_dir
+    
+    def load_paired_dataset(self, text_split, truncate_source=False):
+        text_split, lp = text_split.rsplit('.', 1)       # e.g. "libritext.ltr-ltr"
+        if len(lp.split("-")) == 2:
+            src, tgt = lp.split("-")
+            if src == tgt:
+                logger.warn(f"| trying to load monolingual dataset {text_split}.{lp}, please check your task is right.")
+                paired_dataset = self.load_char_bart_dataset(f"{text_split}.{lp}.{tgt}")
+                return paired_dataset
+            paired_dataset = load_langpair_dataset(
+                self.cfg.text_cfg.text_data,
+                text_split,
+                src,
+                self.text_src_dictionary,
+                tgt,
+                self.text_dictionary,
+                combine=True,
+                dataset_impl=None,
+                upsample_primary=1,
+                left_pad_source=False,
+                left_pad_target=False,
+                max_source_positions=self.cfg.text_cfg.tokens_per_sample,
+                max_target_positions=self.cfg.text_cfg.tokens_per_sample,
+                truncate_source=truncate_source,
+                prepend_bos=False,
+                load_alignments=False,
+                append_source_id=True if self.cfg.text_cfg.prepend_tgt_lang_tag else False,
+                lang_format="<lang:{}>" if self.cfg.text_cfg.prepend_tgt_lang_tag else "[{}]",
+                input_feeding=self.cfg.add_decoder_target,
+            )
+            if self.cfg.text_cfg.mask_text_ratio > 0:
+                # add mask
+                self.mask_idx = self.text_src_dictionary.index("<mask>")
+                mask_whole_words = None
+                if self.cfg.text_cfg.mask_whole_words:
+                    mask_whole_words = get_whole_word_mask(self.cfg.text_cfg, self.text_src_dictionary)
+                elif self.cfg.text_cfg.mask_repeative_tokens:
+                    mask_whole_words = get_repeative_start
+                
+                src_dataset, src_unmasked_dataset = MaskTokensDataset.apply_mask(
+                    paired_dataset.src,
+                    self.text_src_dictionary,
+                    pad_idx=self.text_src_dictionary.pad(),
+                    mask_idx=self.mask_idx,
+                    seed=self.cfg.text_cfg.seed,
+                    mask_prob=self.cfg.text_cfg.mask_text_ratio,
+                    leave_unmasked_prob=self.cfg.text_cfg.leave_unmasked_prob,
+                    random_token_prob=self.cfg.text_cfg.mask_random,
+                    freq_weighted_replacement=self.cfg.text_cfg.freq_weighted_replacement,
+                    mask_whole_words=mask_whole_words,
+                    mask_multiple_length=self.cfg.text_cfg.mask_multiple_length,
+                    mask_stdev=self.cfg.text_cfg.mask_stdev,
+                )
+                tgt_dataset = paired_dataset.tgt if paired_dataset.tgt is not None else src_unmasked_dataset
+                paired_dataset = LanguageTripleDataset(
+                    src_dataset,
+                    src_dataset.sizes,
+                    self.text_src_dictionary,
+                    src_unmasked_dataset,
+                    src_unmasked_dataset.sizes,
+                    self.text_src_dictionary,
+                    tgt_dataset,
+                    tgt_dataset.sizes,
+                    self.text_dictionary,
+                    left_pad_source=False,
+                    left_pad_target=False,
+                    align_dataset=None,
+                    eos=None,
+                    num_buckets=0,
+                    shuffle=True,
+                    pad_to_multiple=1,
+                )
+        else:
+            src, ref, tgt = lp.split("-")
+            paired_dataset = load_langtriple_dataset(
+                self.cfg.text_cfg.text_data,
+                text_split,
+                src,
+                self.text_src_dictionary,
+                ref,
+                self.dictionaries[-1],
+                tgt,
+                self.text_dictionary,
+                combine=True,
+                dataset_impl=None,
+                upsample_primary=1,
+                left_pad_source=False,
+                left_pad_target=False,
+                max_source_positions=self.cfg.text_cfg.tokens_per_sample,
+                max_target_positions=self.cfg.text_cfg.tokens_per_sample,
+                truncate_source=truncate_source,
+                prepend_bos=False,
+                load_alignments=False,
+                append_source_id=True if self.cfg.text_cfg.prepend_tgt_lang_tag else False,
+                lang_format="<lang:{}>" if self.cfg.text_cfg.prepend_tgt_lang_tag else "[{}]",
+            )
+        return paired_dataset
+
+    def load_dataset(self, split: str, epoch=1, **kwargs) -> None:
+        """
+            Create Wav dataset for audio, and Index dataset for phonemized text, 
+            then concatenate them to by fairseq.data.multi_corpus_dataset.MultiCorpusDataset.
+        """
+        speech_splits = split.split('+')[0].split(',')
+        ### 1st, create a speech dataset using STSpeechDataset (modified from HubertDataset)
+        dicts = [self.target_dictionary] if self.cfg.fine_tuning else self.dictionaries
+        pad_list = [dict.pad() for dict in dicts]
+        eos_list = [dict.eos() for dict in dicts]
+        procs = [LabelEncoder(dict) for dict in dicts]
+        if self.cfg.speech_tgt_lang != "":
+            tgt_lang_idx = _lang_token_index(dicts[0], self.cfg.speech_tgt_lang)
+            logger.info(f"Will prepend <{tgt_lang_idx}> at the beginning of prev_output_tokens to replace <eos>")
+        else:
+            tgt_lang_idx = None
+
+
+        # hubert v1: pad_audio=True, random_crop=False;
+        speech_datasets = []
+        for speech_split in speech_splits:
+            paths = [
+                f"{self.get_label_dir()}/{speech_split}.{l}" for l in self.cfg.labels
+            ]
+            speech_datasets.append(
+                HubertDataset(
+                    f"{self.cfg.data}/{speech_split}.tsv",
+                    sample_rate=self.cfg.sample_rate,
+                    label_paths=paths,
+                    label_rates=self.cfg.label_rate,
+                    pad_list=pad_list,
+                    eos_list=eos_list,
+                    label_processors=procs,
+                    max_keep_sample_size=self.cfg.max_keep_size,
+                    min_keep_sample_size=self.cfg.min_sample_size,
+                    max_sample_size=self.cfg.max_sample_size,
+                    pad_audio=self.cfg.pad_audio,
+                    normalize=self.cfg.normalize,
+                    store_labels=self.cfg.store_labels,
+                    random_crop=self.cfg.random_crop,
+                    single_target=self.cfg.single_target,
+                    tgt_dict=dicts[0],
+                    add_decoder_target=self.cfg.add_decoder_target,
+                    fine_tuning=self.cfg.fine_tuning,
+                    tgt_lang_idx=tgt_lang_idx,
+                    tokenizer=self.hubert_tokenizer,
+                )
+            )
+        if len(speech_datasets) > 1:
+            speech_dataset = ConcatDataset(speech_datasets)
+        else:
+            speech_dataset = speech_datasets[0]
+
+        has_text = len(split.split('+')) > 1
+        if not has_text:
+            assert speech_dataset is not None
+            self.datasets[split] = speech_dataset
+            return
+
+        ### 2nd, create paired/mono text datasets using Langpairdataset
+        if split.split('+')[1] != '':
+            paired_splits = [paired_split for paired_split in split.split('+')[1].split(',') if paired_split != '']
+            paired_datasets = [self.load_paired_dataset(paired_split) for paired_split in paired_splits]
+        else:
+            paired_splits, paired_datasets = [], []
+
+        if len(split.split('+')) > 2 and split.split('+')[2] != '':
+            mono_splits = [mono_split for mono_split in split.split('+')[2].split(',') if mono_split != '']
+            mono_datasets = [self.load_paired_dataset(mono_split, truncate_source=self.cfg.text_cfg.truncate_mono_source) for mono_split in mono_splits]
+        else:
+            mono_splits, mono_datasets = [], []
+        
+        assert len(mono_datasets + paired_datasets) > 0, f"split {split} has no text! you should check out for that"
+
+        ### 3rd, if provided, create a supervised dataset with labeled data
+        if len(split.split('+')) > 3 and split.split('+')[3] != '':
+            assert len(paired_splits) > 0, f"supervised dataset can not be loaded without text paired dataset!"
+            tgt = paired_splits[0].rsplit('.', 1)[1].split("-")[1]
+            sup_split = split.split('+')[3]
+
+            sup_dataset = HubertDataset(
+                f"{self.cfg.data}/{sup_split}.tsv",
+                sample_rate=self.cfg.sample_rate,
+                label_paths=[f"{self.get_label_dir()}/{sup_split}.{tgt}"],
+                label_rates=[-1],
+                pad_list=[self.text_dictionary.pad()],
+                eos_list=[self.text_dictionary.eos()],
+                label_processors=[LabelEncoder(self.text_dictionary)],
+                max_keep_sample_size=self.cfg.max_keep_size,
+                min_keep_sample_size=None,
+                max_sample_size=None,
+                pad_audio=True,
+                normalize=self.cfg.normalize,
+                store_labels=self.cfg.store_labels,
+                random_crop=False,
+                single_target=True,
+                tgt_dict=self.text_dictionary,
+                add_decoder_target=self.cfg.add_decoder_target,
+                fine_tuning=True,
+                tgt_lang_idx=None,
+                tokenizer=None,
+            )
+        else:
+            sup_dataset = None
+
+        ### 4th, compose a MultiCorpusDataset
+        dataset_dict, max_positions_dict, distributions, max_tokens_ratios = self.resample_multi_modality_dataset(
+        speech_dataset, sup_dataset, mono_datasets, paired_datasets, mono_splits, paired_splits, epoch=epoch,
+        )
+        self.datasets[split] = MultiCorpusDataset(
+            dataset_dict,
+            max_positions=max_positions_dict,
+            distribution=distributions,
+            max_tokens_ratio=max_tokens_ratios,
+            seed=self.cfg.text_cfg.seed,
+            sort_indices=True,
+        )
+
+
+    def max_positions(self) -> Tuple[int, int]:
+        return (sys.maxsize, sys.maxsize)
+
+    def filter_indices_by_size(
+        self, indices: np.array, *args, **kwargs
+    ) -> np.array:
+        return indices
+
+    def get_batch_iterator(
+        self,
+        dataset,
+        max_tokens=None,
+        max_sentences=None,
+        max_positions=None,
+        ignore_invalid_inputs=False,
+        required_batch_size_multiple=1,
+        seed=1,
+        num_shards=1,
+        shard_id=0,
+        num_workers=0,
+        epoch=1,
+        data_buffer_size=0,
+        disable_iterator_cache=False,
+        skip_remainder_batch=False,
+        grouped_shuffling=False,
+        update_epoch_batch_itr=False,
+    ):
+        """
+        Get an iterator that yields batches of data from the given dataset.
+        Args:
+            dataset (~fairseq.data.FairseqDataset): dataset to batch
+            max_tokens (int, optional): max number of tokens in each batch
+                (default: None).
+            max_sentences (int, optional): max number of sentences in each
+                batch (default: None).
+            max_positions (optional): max sentence length supported by the
+                model (default: None).
+            ignore_invalid_inputs (bool, optional): don't raise Exception for
+                sentences that are too long (default: False).
+            required_batch_size_multiple (int, optional): require batch size to
+                be a multiple of N (default: 1).
+            seed (int, optional): seed for random number generator for
+                reproducibility (default: 1).
+            num_shards (int, optional): shard the data iterator into N
+                shards (default: 1).
+            shard_id (int, optional): which shard of the data iterator to
+                return (default: 0).
+            num_workers (int, optional): how many subprocesses to use for data
+                loading. 0 means the data will be loaded in the main process
+                (default: 0).
+            epoch (int, optional): the epoch to start the iterator from
+                (default: 1).
+            data_buffer_size (int, optional): number of batches to
+                preload (default: 0).
+            disable_iterator_cache (bool, optional): don't cache the
+                EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`)
+                (default: False).
+            skip_remainder_batch (bool, optional): if set, discard the last
+                batch in each training epoch, as the last batch is often smaller than
+                    local_batch_size * distributed_word_size (default: ``True``).
+            grouped_shuffling (bool, optional): group batches with each groups
+                containing num_shards batches and shuffle groups. Reduces difference
+                between sequence lengths among workers for batches sorted by length.
+            update_epoch_batch_itr (bool optional): if true then donot use the cached
+                batch iterator for the epoch
+            
+        Returns:
+            ~fairseq.iterators.EpochBatchIterator: a batched iterator over the
+                given dataset split
+        """
+        if self.fine_tuning or not isinstance(dataset, MultiCorpusDataset):
+            return super().get_batch_iterator(
+                dataset,
+                max_tokens=max_tokens,
+                max_sentences=max_sentences,
+                max_positions=max_positions,
+                ignore_invalid_inputs=ignore_invalid_inputs,
+                required_batch_size_multiple=required_batch_size_multiple,
+                seed=seed,
+                num_shards=num_shards,
+                shard_id=shard_id,
+                num_workers=num_workers,
+                epoch=epoch,
+                data_buffer_size=data_buffer_size,
+                disable_iterator_cache=disable_iterator_cache,
+                skip_remainder_batch=skip_remainder_batch,
+                grouped_shuffling=grouped_shuffling,
+                update_epoch_batch_itr=update_epoch_batch_itr,
+            )
+        
+        can_reuse_epoch_itr = (
+            not disable_iterator_cache
+            and not update_epoch_batch_itr
+            and self.can_reuse_epoch_itr(dataset)
+        )
+        if can_reuse_epoch_itr and dataset in self.dataset_to_epoch_iter:
+            logger.debug("reusing EpochBatchIterator for epoch {}".format(epoch))
+            return self.dataset_to_epoch_iter[dataset]
+
+        assert isinstance(dataset, FairseqDataset)
+
+        # initialize the dataset with the correct starting epoch
+        dataset.set_epoch(epoch)
+
+        # get indices ordered by example size
+        with data_utils.numpy_seed(seed):
+            indices = dataset.ordered_indices()
+
+        # filter examples that are too large
+        if max_positions is not None:
+            indices = self.filter_indices_by_size(
+                indices, dataset, max_positions, ignore_invalid_inputs
+            )
+
+        # create mini-batches with given size constraints
+        batch_sampler = dataset.get_batch_sampler(
+            indices,
+            num_shards,
+            seed,
+            max_tokens=max_tokens,
+            max_sentences=max_sentences,
+            required_batch_size_multiple=required_batch_size_multiple,
+            split_modality_batch=self.cfg.split_modality_batch,
+        )
+
+        # return a reusable, sharded iterator
+        epoch_iter = iterators.EpochBatchIterator(
+            dataset=dataset,
+            collate_fn=dataset.collater,
+            batch_sampler=batch_sampler,
+            seed=seed,
+            num_shards=num_shards,
+            shard_id=shard_id,
+            num_workers=num_workers,
+            epoch=epoch,
+            buffer_size=data_buffer_size,
+            skip_remainder_batch=skip_remainder_batch,
+            disable_shuffling=True,
+            grouped_shuffling=grouped_shuffling,
+        )
+
+        if can_reuse_epoch_itr:
+            self.dataset_to_epoch_iter[dataset] = epoch_iter
+
+        return epoch_iter
+
+    @classmethod
+    def _get_size_ratios(cls, ids: List[str], sizes: List[int], alpha: float = 1.0):
+        """Size ratios for temperature-based sampling
+        (https://arxiv.org/abs/1907.05019)"""
+        _sizes = np.array(sizes)
+        prob = _sizes / _sizes.sum()
+        smoothed_prob = prob ** alpha
+        smoothed_prob = smoothed_prob / smoothed_prob.sum()
+        size_ratio = (smoothed_prob * _sizes.sum()) / _sizes
+
+        o_str = str({_i: f"{prob[i]:.3f}" for i, _i in enumerate(ids)})
+        logger.info(f"original sampling probability: {o_str}")
+        p_str = str({_i: f"{smoothed_prob[i]:.3f}" for i, _i in enumerate(ids)})
+        logger.info(f"balanced sampling probability: {p_str}")
+        sr_str = str({_id: f"{size_ratio[i]:.3f}" for i, _id in enumerate(ids)})
+        logger.info(f"balanced sampling size ratio: {sr_str}")
+        return size_ratio.tolist()
+
+    def resample_multi_modality_dataset(self, speech_dataset, sup_dataset, mono_datasets, paired_datasets, mono_splits, paired_splits, epoch=1, train=True):
+        assert len(mono_datasets+paired_datasets) > 0, f"No text data loaded!"
+
+        if len(mono_datasets) > 1 and self.cfg.text_sampling_alpha != 1.0:
+            size_ratios = self._get_size_ratios(
+                mono_splits, [len(s) for s in mono_datasets], alpha=self.cfg.text_sampling_alpha
+            )
+            mono_datasets = [
+                ResamplingDataset(
+                    d, size_ratio=r, seed=0, epoch=epoch, replace=(r >= 1.0)
+                ) for d, r in zip(mono_datasets, size_ratios)
+            ]
+            
+        if len(paired_datasets) > 1 and self.cfg.text_sampling_alpha != 1.0:
+            size_ratios = self._get_size_ratios(
+                paired_splits, [len(s) for s in paired_datasets], alpha=self.cfg.text_sampling_alpha
+            )
+            paired_datasets = [
+                ResamplingDataset(
+                    d, size_ratio=r, seed=0, epoch=epoch, replace=(r >= 1.0)
+                ) for d, r in zip(paired_datasets, size_ratios)
+            ]
+
+        dataset_list = [speech_dataset, sup_dataset]
+        for datasets in [mono_datasets, paired_datasets]:
+            if len(datasets) > 1:
+                dataset_list.append(ConcatDataset(datasets))
+            elif len(datasets) == 1:
+                dataset_list.append(datasets[0])
+            else:
+                dataset_list.append(None)
+
+        ### match speech/text datasets according to modality
+        dataset_dict = OrderedDict((name, d) for name, d in zip(["speech", "speech_sup", "text_mono", "text_paired"], dataset_list) if d is not None)
+        max_positions_dict = {
+            "speech": None,
+            "speech_sup": None,
+            "text_mono": (self.cfg.text_cfg.tokens_per_sample, self.cfg.text_cfg.tokens_per_sample),
+            "text_paired": (self.cfg.text_cfg.tokens_per_sample, self.cfg.text_cfg.tokens_per_sample),
+        }
+        max_positions_dict = OrderedDict((name, max_positions_dict[name]) for name in dataset_dict.keys())
+        max_tokens_ratios_dict = {
+            "speech": 1.0,
+            "speech_sup": 1.0,
+            "text_mono": 1.0 / 320 / self.cfg.text_cfg.text_maxtokens_ratio,
+            "text_paired": 1.0 / 320 / self.cfg.text_cfg.text_maxtokens_ratio,
+        }
+        max_tokens_ratios = [max_tokens_ratios_dict[name] for name in dataset_dict.keys()]
+        dataset_lens = np.array([len(dataset) for dataset in dataset_dict.values()])
+        dataset_avg_sample_lens = np.array([
+            sum([dataset.num_tokens(i) for i in np.random.randint(low=0, high=len(dataset), size=10000)]) / 10000.0 
+            for dataset in dataset_dict.values()
+        ])
+
+        if not "speech" in dataset_dict:
+            distributions = [l / sum(dataset_lens) for l in dataset_lens]
+        else:
+            ## we just keep the batches of speech and non-speech the same, expand_coef is to ensure speech batches is less than others
+            first_ratio = dataset_lens[0] / sum(dataset_lens)
+            expand_coef = 1.8 if sup_dataset is None else 1.1 * sum(dataset_lens[0:2]) / dataset_lens[0]
+            distributions = [expand_coef * max_tokens_ratios[i] * dataset_avg_sample_lens[0] / l for (i, l) in enumerate(dataset_avg_sample_lens)]
+            distributions[0] = 1.0
+            if sup_dataset is not None:
+                distributions[1] = dataset_lens[1] / dataset_lens[0]
+            distributions = [first_ratio * d for d in distributions]
+
+        logging.info(f"Number samples of datasets is {dataset_lens}")
+        logging.info(f"Avg sample length of datasets is {dataset_avg_sample_lens}")
+        logging.info(f"Sampling distributions is {distributions}")
+        logging.info(f"Maxtokens ratio is {max_tokens_ratios}")
+        return dataset_dict, max_positions_dict, distributions, max_tokens_ratios
+
+    def build_tokenizer(self, cfg=None):
+        logger.info(f"tokenizer: {self.cfg.hubert_tokenizer}")
+        if self.cfg.hubert_tokenizer != "none":
+            return encoders.build_bpe(Namespace(**{"bpe": self.cfg.hubert_tokenizer, "sentencepiece_model": self.cfg.sp_path}))
+        else:
+            return None
+
+    def load_char_bart_dataset(self, split):
+        mono_dataset = data_utils.load_indexed_dataset(
+            f"{self.cfg.text_cfg.text_data}/{split}",
+            self.text_dictionary,
+        )
+        mono_dataset = StripTokenDataset(mono_dataset, self.text_dictionary.eos())
+        mono_dataset = maybe_shorten_dataset(
+            mono_dataset,
+            split,
+            self.cfg.text_cfg.shorten_data_split_list,
+            self.cfg.text_cfg.shorten_method,
+            self.cfg.text_cfg.tokens_per_sample - 2,
+            self.cfg.text_cfg.seed,
+        )
+        logger.info("loaded {} samples from: {}".format(len(mono_dataset), mono_dataset))
+        ### prepend bos and eos to dataset
+        mono_dataset = PrependTokenDataset(mono_dataset, self.text_dictionary.bos())
+        mono_dataset = AppendTokenDataset(mono_dataset, self.text_dictionary.eos())
+        mask_whole_words = (
+            get_whole_word_mask(None, self.text_dictionary)
+            if self.cfg.text_cfg.mask_whole_words
+            else None
+        )
+        lang=self.cfg.speech_tgt_lang
+        mono_dataset = DenoisingDataset(
+            mono_dataset,
+            mono_dataset.sizes,
+            self.text_dictionary,
+            self.mask_idx,
+            mask_whole_words,
+            shuffle=self.cfg.text_cfg.shuffle_instance,
+            seed=self.cfg.text_cfg.seed,
+            args=self.cfg.text_cfg,
+            tgt_lang_idx=_lang_token_index(self.text_dictionary, lang) if self.cfg.text_cfg.prepend_tgt_lang_tag else None,
+        )
+        
+        return mono_dataset

SpeechLM/speechlm/unit_generator.py

+# ----------------------------------------------------------------------------
+# SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data (https://arxiv.org/abs/2209.15329)
+# Github source: https://github.com/microsoft/SpeechT5/tree/main/SpeechLM
+# Code based on fairseq: https://github.com/facebookresearch/fairseq/tree/272c4c5197250997148fb12c0db6306035f166a4
+# 
+# Copyright (c) 2022 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# ----------------------------------------------------------------------------
+
+"""
+Modified form: https://github.com/facebookresearch/fairseq/blob/272c4c5197250997148fb12c0db6306035f166a4/fairseq/sequence_generator.py
+"""
+
+import torch
+import numpy as np
+
+from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
+from fairseq.speech_generator import SpeechGenerator
+
+class NonAutoregressiveUnitGenerator(SpeechGenerator):
+    @torch.no_grad()
+    def generate(self, model, sample, has_targ=False, **kwargs):
+        model.eval()
+
+        bsz, max_src_len = sample["net_input"]["src_tokens"].size()
+        n_frames_per_step = model.encoder.n_frames_per_step
+        out_dim = model.encoder.out_dim
+        raw_dim = out_dim // n_frames_per_step
+
+        logit, logit_post, out_lens, log_dur_out, _, _ = model(
+            src_tokens=sample["net_input"]["src_tokens"],
+            src_lengths=sample["net_input"]["src_lengths"],
+            speaker=sample["speaker"],
+            durations=sample["durations"],
+            pitches=sample["pitches"],
+            energies=sample["energies"],
+        )
+        if logit_post is not None:
+            logit = logit_post
+
+        logit = logit.view(bsz, -1, raw_dim)
+        pred = logit.argmax(dim=-1)
+
+        ## get duration prediction
+        src_tokens = sample["net_input"]["src_tokens"]
+        src_lengths = sample["net_input"]["src_lengths"]
+        padding_mask = src_tokens.eq(model.encoder.padding_idx)
+        d_factor = 1.0 ## set by model
+        dur_out = torch.clamp(
+            torch.round((torch.exp(log_dur_out) - 1) * d_factor).long(), min=0
+        )
+        dur_out.masked_fill_(padding_mask, 0)
+        x = src_tokens.unsqueeze(-1)
+        x, src_out_lens = model.encoder.var_adaptor.length_regulator(x, dur_out)
+        fa_src_tokens = x.view(bsz, -1)
+        
+        finalized = [
+            {
+                "unit": pred[b, :l],
+                "fa_src": fa_src_tokens[b, :l],
+                "duration": dur_out[b, :L],
+            }
+            for b, l, L in zip(range(bsz), out_lens, src_lengths)
+        ]
+
+        return finalized

SpeechT5/README.md

+# SpeechT5
+
+<!--**Pre-trained models for speech related tasks**-->
+
+ [**SpeechT5**](https://arxiv.org/abs/2110.07205): **Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing**
+
+Official PyTorch implementation and pretrained models of SpeechT5
+
+- Oct 2021: release preprint in [arXiv](https://arxiv.org/abs/2110.07205)
+- Feb 2022: accepted by [ACL 2022](https://www.2022.aclweb.org/)
+
+
+## Pre-Trained Models
+
+|  Model   |               Pre-training Dataset               | Fine-tuning Dataset | Model |
+| :------: | :----------------------------------------------: | :-----------------: | :-----: |
+| SpeechT5 Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [LibriSpeech LM Dataset](https://www.openslr.org/11/) |          -          | [HuggingFace](https://huggingface.co/ajyy/SpeechT5/resolve/main/speecht5_base.pt)<br /> [Google Drive](https://drive.google.com/file/d/1Sq00uZ1pw6Z4OUaqhOWzQEJxIVWgAO5U/view?usp=sharing)  |
+| SpeechT5 Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [LibriSpeech LM Dataset](https://www.openslr.org/11/) | [100 hrs LibriSpeech](http://www.openslr.org/12) | [HuggingFace](https://huggingface.co/ajyy/SpeechT5/resolve/main/speecht5_base_asr.pt)<br /> [Google Drive](https://drive.google.com/file/d/1qLKJ81JPWOGf1MHfjSmgtZyqqTqgI6kT/view?usp=sharing)  |
+| SpeechT5 Large | [60k hrs Libri-Light](https://github.com/facebookresearch/libri-light) + [LibriSpeech LM Dataset](https://www.openslr.org/11/) |          -          | [Google Drive](https://drive.google.com/file/d/1M79b1jetSPOVxWVMIX-y0URvDjNskZKp/view?usp=sharing)  |
+
+## Language Model and Vocabulary
+|  Model   |  Dataset | Model | Vocabulary | SPM Model |
+| :------: | :------: | :---: | :--------: | :-------: |
+| LM | [LibriSpeech LM Dataset](https://www.openslr.org/11/) | [LM Model](https://drive.google.com/uc?export=download&id=1y0TGnKAMKUW5C8l8yrvGjh9RRZETPdv7)  | [Vocabulary](https://drive.google.com/uc?export=download&id=19hcQ58RHZ6CssxF8Qp6yEF1NW_AXxObK) | [SPM Model](https://drive.google.com/uc?export=download&id=1wClgQjXXoU2lmpbaEa1v2SqMbg7cAutq) |
+
+
+## Setup
+```
+git submodule update --init SpeechT5/fairseq
+cd SpeechT5/
+pip install --editable fairseq/
+pip install espnet
+```
+
+
+
+## Load Pre-Trained Models
+
+```python
+import torch
+from speecht5.tasks.speecht5 import SpeechT5Task
+from speecht5.models.speecht5 import T5TransformerModel
+
+checkpoint = torch.load('/path/to/speecht5_checkpoint')
+
+checkpoint['cfg']['task'].t5_task = 'pretrain'
+checkpoint['cfg']['task'].hubert_label_dir = "/path/to/hubert_label"
+checkpoint['cfg']['task'].data = "/path/to/tsv_file"
+
+task = SpeechT5Task.setup_task(checkpoint['cfg']['task'])
+model = T5TransformerModel.build_model(checkpoint['cfg']['model'], task)
+model.load_state_dict(checkpoint['model'])
+```
+
+## Data Preparation
+
+### Speech data and S2T Data
+Please follow the steps for preparing wav2vec 2.0 manifest in [here](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#prepare-training-data-manifest) and preparing HuBERT label in [here](https://github.com/facebookresearch/fairseq/tree/main/examples/hubert/simple_kmeans).
+
+We add a third column for the speaker embedding, which is provided in [here](https://drive.google.com/uc?export=download&id=16QOUURZBrW7-GYbVG_gXt3mTMlZmQoH0).
+It includes the speaker embeddings for 960hr training data and dev-other data of LibriSpeech.
+
+We also provide example manifests for your reference in [here](https://drive.google.com/drive/folders/1Ja08XjOHe6vP8lZtLVrJM8173aPQCR_y?usp=sharing).
+
+### Text Data
+Please use [fairseq-preprocess](https://fairseq.readthedocs.io/en/latest/command_line_tools.html#fairseq-preprocess) to generate the index and bin files of the text data. Note that we use sentencepiece to pre-process the text, so please refer to [here](https://github.com/microsoft/SpeechT5/tree/main/SpeechT5#language-model-and-vocabulary) to download the SPM model and dictionary for preparing text data. This means you firstly need to use the SPM model to process the text and then use [fairseq-preprocess](https://fairseq.readthedocs.io/en/latest/command_line_tools.html#fairseq-preprocess) with the provided dictionary to get the index and bin files.
+
+## Pre-Training
+
+### 960hr LibriSpeech + LibriSpeech-LM
+
+```
+DATA_ROOT=
+SAVE_DIR=
+LABEL_DIR=
+TRAIN_SET="speech_train|text_train"
+VALID_SET="speech_valid|text_valid"
+
+
+fairseq-train ${DATA_ROOT} \
+  --save-dir ${SAVE_DIR} \
+  --tensorboard-logdir ${SAVE_DIR} \
+  --train-subset ${TRAIN_SET} \
+  --valid-subset ${VALID_SET} \
+  --hubert-label-dir ${LABEL_DIR} \
+  --distributed-world-size 32 \
+  --distributed-port 0 \
+  --ddp-backend legacy_ddp \
+  --user-dir SpeechT5/speecht5 \
+  --log-format json \
+  --seed 1337 \
+  --fp16 \
+  \
+  --task speecht5 \
+  --t5-task pretrain \
+  --label-rates 50 \
+  --sample-rate 16000 \
+  --random-crop \
+  \
+  --num-workers 0 \
+  --max-tokens 1400000 \
+  --max-speech-sample-size 250000 \
+  --update-freq 2 \
+  --batch-ratio "[1,0.0086]" \
+  \
+  --criterion speecht5 \
+  --optimizer adam \
+  --reset-optimizer \
+  --adam-betas "(0.9, 0.98)" \
+  --adam-eps 1e-06 \
+  --weight-decay 0.01 \
+  --power 1 \
+  --clip-norm 5.0 \
+  --lr 0.0002 \
+  --lr-scheduler polynomial_decay \
+  \
+  --max-update 800000 \
+  --warmup-updates 64000 \
+  --total-num-update 800000 \
+  --save-interval-updates 3000 \
+  --skip-invalid-size-inputs-valid-test \
+  --required-batch-size-multiple 1 \
+  \
+  --arch t5_transformer_base \
+  --share-input-output-embed \
+  --find-unused-parameters \
+  --bert-init \
+  --relative-position-embedding \
+  --use-codebook \
+  --codebook-prob 0.1 \
+  --loss-weights="[10,0.1]" \
+  --max-text-positions 600 \
+```
+
+## Finetune
+
+### ASR
+
+The fine-tuned ASR model can be used directly using Hugging Face Transformers. The checkpoint is available at [hf.co/microsoft/speecht5_asr](https://huggingface.co/microsoft/speecht5_asr). An interactive demo is [available here](https://huggingface.co/spaces/Matthijs/speecht5-asr-demo).
+
+#### Training
+
+```
+DATA_ROOT=
+SAVE_DIR=
+TRAIN_SET=
+VALID_SET=
+LABEL_DIR=
+BPE_TOKENIZER=
+USER_DIR=
+PT_CHECKPOINT_PATH=
+
+mkdir -p ${SAVE_DIR}
+fairseq-train ${DATA_ROOT} \
+  --save-dir ${SAVE_DIR} \
+  --tensorboard-logdir ${SAVE_DIR} \
+  --train-subset ${TRAIN_SET} \
+  --valid-subset ${VALID_SET} \
+  --hubert-label-dir ${LABEL_DIR} \
+  --distributed-world-size 8 \
+  --distributed-port 0 \
+  --ddp-backend legacy_ddp \
+  --user-dir ${USER_DIR} \
+  --log-format json \
+  --seed 1 \
+  --fp16 \
+  \
+  --task speecht5 \
+  --t5-task s2t \
+  --sample-rate 16000 \
+  --num-workers 0 \
+  --max-tokens 1600000 \
+  --update-freq 2 \
+  --bpe-tokenizer ${BPE_TOKENIZER} \
+  \
+  --criterion speecht5 \
+  --report-accuracy \
+  --zero-infinity \
+  --ce-weight 0.5 \
+  --ctc-weight 0.5 \
+  --sentence-avg \
+  \
+  --optimizer adam \
+  --adam-betas "(0.9, 0.98)" \
+  --adam-eps 1e-08 \
+  --weight-decay 0.1 \
+  --clip-norm 25.0 \
+  --lr 0.00006 \
+  --lr-scheduler tri_stage \
+  --phase-ratio "[0.1, 0.4, 0.5]" \
+  --final-lr-scale 0.05 \
+  \
+  --max-update 80000 \
+  --max-text-positions 600 \
+  --required-batch-size-multiple 1 \
+  --save-interval-updates 3000 \
+  --skip-invalid-size-inputs-valid-test \
+  \
+  --arch t5_transformer_base_asr \
+  --share-input-output-embed \
+  --find-unused-parameters \
+  --bert-init \
+  --relative-position-embedding \
+  --freeze-encoder-updates 13000 \
+  \
+  --keep-last-epochs 10 \
+  --feature-grad-mult 1.0 \
+  --best-checkpoint-metric s2t_accuracy \
+  --maximize-best-checkpoint-metric \
+  --finetune-from-model ${PT_CHECKPOINT_PATH}
+```
+
+#### Inference
+Note that joint CTC/Decoder inference is only supported when batch size is 1.
+
+```
+CHECKPOINT_PATH=
+DATA_ROOT=
+SUBSET=
+BPE_TOKENIZER=
+LABEL_DIR=
+USER_DIR=
+BEAM=
+MAX_TOKENS=
+CTC_WEIGHT=
+LM_WEIGHT=
+LM_PATH=
+
+fairseq-generate ${DATA_ROOT} \
+  --gen-subset ${SUBSET} \
+  --bpe-tokenizer ${BPE_TOKENIZER} \
+  --user-dir ${USER_DIR} \
+  --task speecht5 \
+  --t5-task s2t \
+  --path ${CHECKPOINT_PATH} \
+  --hubert-label-dir ${LABEL_DIR} \
+  --ctc-weight ${CTC_WEIGHT} \
+  --lm-weight ${LM_WEIGHT} \
+  --lm-path ${LM_PATH} \
+  --max-tokens ${MAX_TOKENS} \
+  --beam ${BEAM} \
+  --scoring wer \
+  --max-len-a 0 \
+  --max-len-b 620 \
+  --sample-rate 16000
+```
+
+### TTS
+
+The manifest and pre-trained vocoder can be found in [huggingface](https://huggingface.co/mechanicalsea/speecht5-tts), which may be helpful to reproduce the results of SpeechT5 TTS model.
+
+We also provide re-implementation of TTS fine-tuned model [speecht5_tts.pt](https://huggingface.co/mechanicalsea/speecht5-tts/blob/main/speecht5_tts.pt), but with a smaller batch size or max updates, which can be helpful.
+
+This fine-tuned TTS model can also be used directly using Hugging Face Transformers. The checkpoint is available at [hf.co/microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts). An interactive demo is [available here](https://huggingface.co/spaces/Matthijs/speecht5-tts-demo). Also see [this Colab notebook](https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ) on how to fine-tune SpeechT5 for TTS using Hugging Face.
+
+#### Training
+
+```
+DATA_ROOT=
+SAVE_DIR=
+TRAIN_SET=
+VALID_SET=
+LABEL_DIR=
+BPE_TOKENIZER=
+USER_DIR=
+PT_CHECKPOINT_PATH=
+
+fairseq-train ${DATA_ROOT} \
+  --save-dir ${SAVE_DIR} \
+  --tensorboard-logdir ${SAVE_DIR} \
+  --train-subset ${TRAIN_SET} \
+  --valid-subset ${VALID_SET} \
+  --hubert-label-dir ${LABEL_DIR} \
+  --distributed-world-size 8 \
+  --distributed-port 0 \
+  --ddp-backend legacy_ddp \
+  --user-dir ${USER_DIR} \
+  --log-format json \
+  --seed 1 \
+  --fp16 \
+  \
+  --task speecht5 \
+  --t5-task t2s \
+  --sample-rate 16000 \
+  --num-workers 4 \
+  --max-tokens 3200000 \
+  --update-freq 1 \
+  --bpe-tokenizer ${BPE_TOKENIZER} \
+  --max-tokens-valid 3200000 \
+  \
+  --criterion speecht5 \
+  --use-guided-attn-loss \
+  --report-accuracy \
+  --sentence-avg \
+  \
+  --optimizer adam \
+  --adam-betas "(0.9, 0.98)" \
+  --dropout 0.15 \
+  --activation-dropout 0.15 \
+  --attention-dropout 0.15 \
+  --encoder-layerdrop 0.0 \
+  --decoder-layerdrop 0.0 \
+  --weight-decay 0.0 \
+  --clip-norm 25.0 \
+  --lr 0.0001 \
+  --lr-scheduler inverse_sqrt \
+  --warmup-updates 10000 \
+  --feature-grad-mult 1.0 \
+  \
+  --max-update 120000 \
+  --max-text-positions 600 \
+  --min-speech-sample-size 1056 \
+  --max-speech-sample-size 480256 \
+  --max-speech-positions 1876 \
+  --required-batch-size-multiple 1 \
+  --skip-invalid-size-inputs-valid-test \
+  --keep-last-epochs 10 \
+  --validate-after-updates 20000 \
+  --validate-interval 50 \
+  --log-interval 10 \
+  \
+  --arch t5_transformer_base_asr \
+  --share-input-output-embed \
+  --find-unused-parameters \
+  --bert-init \
+  --relative-position-embedding \
+  --freeze-encoder-updates 20000 \
+  \
+  --finetune-from-model ${PT_CHECKPOINT_PATH}
+```
+
+#### Inference
+
+Generating speech is available only if batch size is 1.
+
+```
+SPEECHT5_CODE_DIR=
+CHECKPOINT_PATH=
+DATA_ROOT=
+SUBSET=
+BPE_TOKENIZER=
+LABEL_DIR=
+USER_DIR=
+RESULTS_PATH=
+
+python3 ${SPEECHT5_CODE_DIR}/SpeechT5/scripts/generate_speech.py ${DATA_ROOT} \
+  --gen-subset ${SUBSET} \
+  --bpe-tokenizer ${BPE_TOKENIZER} \
+  --user-dir ${USER_DIR} \
+  --task speecht5 \
+  --t5-task t2s \
+  --path ${CHECKPOINT_PATH} \
+  --hubert-label-dir ${LABEL_DIR} \
+  --batch-size 1 \
+  --results-path ${RESULTS_PATH} \
+  --sample-rate 16000
+```
+
+### ST
+
+Here we follow [fairseq/speech_to_text/mustc](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_text/docs/mustc_example.md#data-preparation) to generate vocabulary, which is different from the pre-trained models. So we randomly initilize the embedding table of the pre-trained models during fine-tuning.
+
+#### Training
+
+```
+DATA_ROOT=
+SAVE_DIR=
+TRAIN_SET=
+VALID_SET=
+LABEL_DIR=
+BPE_TOKENIZER=
+USER_DIR=
+PT_CHECKPOINT_PATH=
+
+fairseq-train ${DATA_ROOT} \
+  --save-dir ${SAVE_DIR} \
+  --tensorboard-logdir ${SAVE_DIR} \
+  --train-subset ${TRAIN_SET} \
+  --valid-subset ${VALID_SET} \
+  --hubert-label-dir ${LABEL_DIR} \
+  --distributed-world-size 8 \
+  --distributed-port 0 \
+  --ddp-backend legacy_ddp \
+  --user-dir ${USER_DIR} \
+  --log-format json \
+  --seed 1 \
+  --fp16 \
+  \
+  --task speecht5 \
+  --t5-task s2t \
+  --sample-rate 16000 \
+  --num-workers 6 \
+  --max-tokens 480256 \
+  --update-freq 4 \
+  --bpe-tokenizer ${BPE_TOKENIZER} \
+  --max-tokens-valid 3200000 \
+  \
+  --criterion speecht5 \
+  --label-smoothing 0.1 \
+  --report-accuracy \
+  --sentence-avg \
+  \
+  --optimizer adam \
+  --adam-betas "(0.9, 0.98)" \
+  --weight-decay 0.0 \
+  --clip-norm 10.0 \
+  --lr 0.0002 \
+  --lr-scheduler inverse_sqrt \
+  --warmup-updates 25000 \
+  --feature-grad-mult 1.0 \
+  \
+  --max-update 80000 \
+  --max-text-positions 600 \
+  --min-speech-sample-size 1056 \
+  --max-speech-sample-size 480256 \
+  --max-speech-positions 1876 \
+  --required-batch-size-multiple 1 \
+  --skip-invalid-size-inputs-valid-test \
+  --keep-last-epochs 10 \
+  \
+  --arch t5_transformer_base_asr \
+  --share-input-output-embed \
+  --find-unused-parameters \
+  --bert-init \
+  --relative-position-embedding \
+  --freeze-encoder-updates 0 \
+  --mask-prob 0.5 \
+  --mask-channel-prob 0.5 \
+  \
+  --finetune-from-model ${PT_CHECKPOINT_PATH}
+```
+
+#### Inference
+
+```
+FAIRSEQ_DIR=
+CHECKPOINT_PATH=
+DATA_ROOT=
+BPE_TOKENIZER=
+LABEL_DIR=
+USER_DIR=
+MAX_TOKENS=
+
+python3 ${FAIRSEQ_DIR}/scripts/average_checkpoints.py \
+  --inputs ${CHECKPOINT_PATH} \
+  --num-epoch-checkpoints 10 \
+  --output ${CHECKPOINT_PATH}/avg_last_10_checkpoint.pt
+
+fairseq-generate ${DATA_ROOT} \
+        --gen-subset tst-COMMON \
+        --bpe-tokenizer ${BPE_TOKENIZER} \
+        --user-dir ${USER_DIR} \
+        --task speecht5 \
+        --t5-task s2t \
+        --path ${CHECKPOINT_PATH}/avg_last_10_checkpoint.pt \
+        --hubert-label-dir ${LABEL_DIR} \
+        --max-tokens ${MAX_TOKENS} \
+        --min-speech-sample-size 1056 \
+        --beam 5 \
+        --scoring sacrebleu \
+        --max-len-a 0 \
+        --max-len-b 620 \
+        --sample-rate 16000
+```
+
+### VC
+
+The manifest and pre-trained vocoder can be found in [huggingface](https://huggingface.co/mechanicalsea/speecht5-vc), which may be helpful to reproduce the results of SpeechT5 VC model.
+
+We also provide re-implementation of VC fine-tuned model [speecht5_vc.pt](https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/speecht5_vc.pt), but with a smaller batch size or max updates, which can be helpful.
+
+This fine-tuned VC model can also be used directly using Hugging Face Transformers. The checkpoint is available at [hf.co/microsoft/speecht5_vc](https://huggingface.co/microsoft/speecht5_vc). An interactive demo is [available here](https://huggingface.co/spaces/Matthijs/speecht5-vc-demo).
+
+#### Training
+
+
+```
+DATA_ROOT=
+SAVE_DIR=
+TRAIN_SET=
+VALID_SET=
+LABEL_DIR=
+BPE_TOKENIZER=
+USER_DIR=
+PT_CHECKPOINT_PATH=
+
+fairseq-train ${DATA_ROOT} \
+        --save-dir ${SAVE_DIR} \
+        --tensorboard-logdir ${SAVE_DIR} \
+        --train-subset ${TRAIN_SET} \
+        --valid-subset ${VALID_SET} \
+        --hubert-label-dir ${LABEL_DIR} \
+        --distributed-world-size 8 \
+        --distributed-port 0 \
+        --ddp-backend legacy_ddp \
+        --user-dir ${USER_DIR} \
+        --log-format json \
+        --seed 1 \
+        --fp16 \
+        \
+        --task speecht5 \
+        --t5-task s2s \
+        --sample-rate 16000 \
+        --num-workers 4 \
+        --max-tokens 1280000 \
+        --update-freq 3 \
+        --max-tokens-valid 1280000 \
+        \
+        --criterion speecht5 \
+        --use-guided-attn-loss \
+        --report-accuracy \
+        --sentence-avg \
+        \
+        --optimizer adam \
+        --dropout 0.2 \
+        --activation-dropout 0.2 \
+        --attention-dropout 0.2 \
+        --encoder-layerdrop 0.05 \
+        --decoder-layerdrop 0.0 \
+        --clip-norm 1.0 \
+        --lr 0.0001 \
+        --lr-scheduler inverse_sqrt \
+        --warmup-updates 6000 \
+        --feature-grad-mult 1.0 \
+        \
+        --max-update 60000 \
+        --max-text-positions 600 \
+        --min-speech-sample-size 1056 \
+        --max-speech-sample-size 480256 \
+        --max-speech-positions 1876 \
+        --required-batch-size-multiple 1 \
+        --skip-invalid-size-inputs-valid-test \
+        --keep-last-epochs 10 \
+        --save-interval-updates 10000 \
+        --disable-validation \
+        --log-interval 10 \
+        \
+        --arch t5_transformer_base_asr \
+        --share-input-output-embed \
+        --find-unused-parameters \
+        --bert-init \
+        --relative-position-embedding \
+        --mask-prob 0.0 \
+        --mask-channel-prob 0.0 \
+        \
+        --finetune-from-model ${PT_CHECKPOINT_PATH}
+```
+
+#### Inference
+
+Generating speech is available only if batch size is 1.
+
+```
+SPEECHT5_CODE_DIR=
+CHECKPOINT_PATH=
+DATA_ROOT=
+SUBSET=
+LABEL_DIR=
+USER_DIR=
+RESULTS_PATH=
+
+python3 ${SPEECHT5_CODE_DIR}/SpeechT5/scripts/generate_speech.py ${DATA_ROOT} \
+        --gen-subset test \
+        --user-dir ${USER_DIR} \
+        --task speecht5 \
+        --t5-task s2s \
+        --path ${CHECKPOINT_PATH} \
+        --hubert-label-dir ${LABEL_DIR} \
+        --batch-size 1 \
+        --results-path ${RESULTS_PATH} \
+        --sample-rate 16000
+```
+
+### SID
+
+The manifest can be found in [huggingface](https://huggingface.co/mechanicalsea/speecht5-sid), which may be helpful to reproduce the results of SpeechT5 SID model.
+
+We also provide re-implementation of SID fine-tuned model [speecht5_sid.pt](https://huggingface.co/mechanicalsea/speecht5-sid/blob/main/speecht5_sid.pt) with training log and results, **but in a smaller batch size**, which can be helpful.
+
+#### Training
+
+
+```
+DATA_ROOT=
+SAVE_DIR=
+TRAIN_SET=
+VALID_SET=
+USER_DIR=
+PT_CHECKPOINT_PATH=
+
+mkdir -p ${SAVE_DIR}
+
+fairseq-train ${DATA_ROOT} \
+  --save-dir ${SAVE_DIR} \
+  --tensorboard-logdir ${SAVE_DIR} \
+  --train-subset ${TRAIN_SET} \
+  --valid-subset ${VALID_SET} \
+  --user-dir ${USER_DIR} \
+  --distributed-world-size 8 \
+  --distributed-port 0 \
+  --ddp-backend legacy_ddp \
+  --log-format json \
+  --seed 1 \
+  --fp16 \
+  \
+  --task speecht5 \
+  --t5-task s2c \
+  --sample-rate 16000 \
+  --num-workers 4 \
+  --batch-size 8 \
+  --update-freq 2 \
+  --data-buffer-size 0 \
+  \
+  --criterion speecht5 \
+  --report-accuracy \
+  --best-checkpoint-metric "s2c_accuracy" \
+  --maximize-best-checkpoint-metric \
+  \
+  --optimizer adam \
+  --dropout 0.1 \
+  --activation-dropout 0.1 \
+  --attention-dropout 0.1 \
+  --encoder-layerdrop 0.05 \
+  --lr-scheduler triangular \
+  --max-lr 2e-4 \
+  --lr-period-updates 60000 \
+  --lr-shrink 0.5 \
+  --lr 1e-8 \
+  --feature-grad-mult 1.0 \
+  --weight-decay 0.1 \
+  \
+  --max-update 60000 \
+  --max-text-positions 600 \
+  --max-speech-positions 8000 \
+  --required-batch-size-multiple 1 \
+  --skip-invalid-size-inputs-valid-test \
+  --save-interval-updates 10000 \
+  --validate-after-updates 20000 \
+  --no-epoch-checkpoints \
+  --log-interval 10 \
+  \
+  --arch t5_transformer_base_asr \
+  --share-input-output-embed \
+  --find-unused-parameters \
+  --bert-init \
+  --relative-position-embedding \
+  --mask-prob 0.0 \
+  --mask-channel-prob 0.0 \
+  --sid-no-pooling-bn \
+  --sid-no-embed-postnet \
+  \
+  --finetune-from-model ${PT_CHECKPOINT_PATH}
+```
+
+#### Inference
+
+
+```
+CHECKPOINT_PATH=
+DATA_ROOT=
+SUBSET=
+USER_DIR=
+RESULTS_PATH=
+
+mkdir -p ${RESULTS_PATH}
+
+python scripts/generate_class.py ${DATA_ROOT} \
+  --gen-subset ${SUBSET} \
+  --user-dir ${USER_DIR} \
+  --log-format json \
+  --task speecht5 \
+  --t5-task s2c \
+  --path ${CHECKPOINT_PATH} \
+  --results-path ${RESULTS_PATH} \
+  --batch-size 1 \
+  --max-speech-positions 8000 \
+  --sample-rate 16000
+```
+
+## License
+
+This project is licensed under the license found in the LICENSE file in the root directory of this source tree.
+Portions of the source code are based on the [FAIRSEQ](https://github.com/pytorch/fairseq) and [ESPnet](https://github.com/espnet/espnet) projects.
+
+[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
+
+### Reference
+
+If you find our work is useful in your research, please cite the following paper:
+
+```bibtex
+@article{Ao2021SpeechT5,
+  title   = {SpeechT5: Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing},
+  author  = {Junyi Ao and Rui Wang and Long Zhou and Chengyi Wang and Shuo Ren and Yu Wu and Shujie Liu and Tom Ko and Qing Li and Yu Zhang and Zhihua Wei and Yao Qian and Jinyu Li and Furu Wei},
+  eprint={2110.07205},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  year={2021}
+}
+```
+
+### Contact Information
+
+For help or issues using SpeechT5 models, please submit a GitHub issue.
+
+For other communications related to SpeechT5, please contact Long Zhou (`lozhou@microsoft.com`).

SpeechT5/asr_train/train.tsv

+/public/home/changhl/dataset/LibriSpeech/dev-clean
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SpeechT5/asr_train/valid.ltr

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SpeechT5/asr_train/valid.tsv

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+2086/149220/2086-149220-0029.flac	89280
+2086/149220/2086-149220-0016.flac	120320
+8297/275154/8297-275154-0010.flac	121760
+2277/149897/2277-149897-0023.flac	52960
+2277/149896/2277-149896-0005.flac	89600
+422/122949/422-122949-0025.flac	285600
+6241/61943/6241-61943-0001.flac	51520
+6241/61943/6241-61943-0024.flac	117760
+2902/9006/2902-9006-0017.flac	380160
+5895/34629/5895-34629-0022.flac	96640	
+5338/24640/5338-24640-0003.flac	102720	
+5694/64038/5694-64038-0004.flac	153280	
+5694/64029/5694-64029-0026.flac	77120
+84/121550/84-121550-0032.flac	132000
+84/121550/84-121550-0005.flac	139439
+84/121550/84-121550-0018.flac	151360	
\ No newline at end of file

SpeechT5/asr_train/valid.txt

+THE MEN WERE AS HANDSOME AS THE WOMEN BEAUTIFUL
+I DON'T WONDER YOU WERE AFRAID TO TELL ME SHE BEGAN YOU DON'T LOVE ME YOU'VE NEVER LOVED ME I WAS AN IDIOT TO BELIEVE YOU DID
+WITH THE INSIGHT OF A KINDRED TEMPERAMENT HE PRONOUNCED HIS VERDICT
+THE LOYAL FRENZY FELL UPON THE THREE QUIET WOMEN AND THEY COULD NOT DO TOO MUCH FOR THEIR COUNTRY
+NOW LET'S BE BRAVE AND ENJOY EVERY MINUTE OF IT
+IT RELIEVED HIM FOR A WHILE
+SO HE'S A FRIEND OF YOURS EH
+OH YOU MINISTERS OF CHRIST WOLVES IN SHEEP'S CLOTHING YOU SHALL BE JUDGED FOR THIS
+I HAVE WAITED LONG FOR YOU
+THE BOYS WERE NOW ALL ANXIETY TO START WHILE THE PONIES AFTER THEIR SUNDAY REST WERE ALMOST AS FULL OF LIFE AS WERE THEIR OWNERS
+AND AS SOON AS THEIR PARENTS HAD GONE TO SLEEP HE GOT UP PUT ON HIS COAT AND UNBARRING THE BACK DOOR WENT OUT
+HER SKIN WAS BROWN TOO AND IN HER CHEEKS SHE HAD A GLOW OF RICH DARK COLOR
+TOM'S EYES FOCUSED IN HORROR ON THE WRECKAGE ENVELOPED BY STILL BILLOWING DUST
+I CAN ASSURE YOU THAT THIS IS A MODERN FACE AND ONE WHICH YOU WILL VERY PROBABLY MEET
+MISS HEPZIBAH I SUPPOSE WILL INTERWEAVE THE FACT WITH HER OTHER TRADITIONS AND SET IT DOWN THAT THE FOWLS KNOW YOU TO BE A PYNCHEON
+WHATEVER REVIVING EFFECT IT MIGHT OTHERWISE HAVE PRODUCED ON HIM IT MADE NO CHANGE IN THE THREATENING GLOOM OF HIS MANNER
+IT WAS THE WORST SUNDAY HE HAD SPENT IN HIS LIFE
+MANY LITTLE WRINKLES GATHERED BETWEEN HIS EYES AS HE CONTEMPLATED THIS AND HIS BROW MOISTENED
+PROFOUND SUFFERING MAKES NOBLE IT SEPARATES ONE OF THE MOST REFINED FORMS OF DISGUISE IS EPICURISM ALONG WITH A CERTAIN OSTENTATIOUS BOLDNESS OF TASTE WHICH TAKES SUFFERING LIGHTLY AND PUTS ITSELF ON THE DEFENSIVE AGAINST ALL THAT IS SORROWFUL AND PROFOUND
+BUT IN THE CAUSE OF SCIENCE MEN ARE EXPECTED TO SUFFER
+I HAVE NOT THE SLIGHTEST DOUBT THAT IN HIGH WINDS ITS RED TILES WERE BLOWN OUT TO THE GREAT ANNOYANCE OF THE PASTOR AND CONGREGATION
+CLIMATE BAD EXAMPLE AND THE LUXURY OF POWER DEGRADED THEM IN ONE CENTURY INTO A RACE OF HELPLESS AND DEBAUCHED SLAVE HOLDERS DOOMED TO UTTER EXTERMINATION BEFORE THE SEMI GOTHIC ARMIES OF BELISARIUS AND WITH THEM VANISHED THE LAST CHANCE THAT THE GOTHIC RACES WOULD EXERCISE ON THE EASTERN WORLD THE SAME STERN YET WHOLESOME DISCIPLINE UNDER WHICH THE WESTERN HAD BEEN RESTORED TO LIFE
+URSUS WAS SATISFIED WITH THE APPLAUSE OF SOUTHWARK BUT BY NO MEANS ASTONISHED
+MISTER MORTON SEEMED PARTICULARLY STRUCK WITH THE ACCOUNT OF WAVERLEY'S VISIT TO DONALD BEAN LEAN
+WE SAW THE UNITED STATES FLAG FLYING FROM THE RAMPARTS AND THOUGHT THAT YANK WOULD PROBABLY BE ASLEEP OR CATCHING LICE OR MAYBE ENGAGED IN A GAME OF SEVEN UP
+AS I WENT BACK TO THE FIELD HOSPITAL I OVERTOOK ANOTHER MAN WALKING ALONG
+SO LOW HE FELL THAT ALL APPLIANCES FOR HIS SALVATION WERE ALREADY SHORT SAVE SHOWING HIM THE PEOPLE OF PERDITION
+SOME APPREHENSION KEEPS YOU MARVELLING BUT THE PSALM DELECTASTI GIVETH LIGHT WHICH HAS THE POWER TO UNCLOUD YOUR INTELLECT
+THE SECOND WAS AS IF HER FLESH AND BONES HAD ALL BEEN FASHIONED OUT OF EMERALD THE THIRD APPEARED AS SNOW BUT NEWLY FALLEN

SpeechT5/fairseq/.github/ISSUE_TEMPLATE.md

+## 👉 [Please follow one of these issue templates](https://github.com/pytorch/fairseq/issues/new/choose) 👈
+
+Note: to keep the backlog clean and actionable, issues may be immediately closed if they do not follow one of the above issue templates.

SpeechT5/fairseq/.github/ISSUE_TEMPLATE/bug_report.md

+---
+name: 🐛 Bug Report
+about: Submit a bug report to help us improve
+labels: 'bug, needs triage'
+---
+
+## 🐛 Bug
+
+<!-- A clear and concise description of what the bug is. -->
+
+### To Reproduce
+
+Steps to reproduce the behavior (**always include the command you ran**):
+
+1. Run cmd '....'
+2. See error
+
+<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
+
+
+#### Code sample
+<!-- Ideally attach a minimal code sample to reproduce the decried issue. 
+Minimal means having the shortest code but still preserving the bug. -->
+
+### Expected behavior
+
+<!-- A clear and concise description of what you expected to happen. -->
+
+### Environment
+
+ - fairseq Version (e.g., 1.0 or master):
+ - PyTorch Version (e.g., 1.0)
+ - OS (e.g., Linux):
+ - How you installed fairseq (`pip`, source):
+ - Build command you used (if compiling from source):
+ - Python version:
+ - CUDA/cuDNN version:
+ - GPU models and configuration:
+ - Any other relevant information:
+
+### Additional context
+
+<!-- Add any other context about the problem here. -->