icefall/egs/wenetspeech4tts/TTS/prepare.sh

#!/usr/bin/env bash

set -eou pipefail

# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python

stage=1
stop_stage=4

dl_dir=$PWD/download

dataset_parts="Premium"  # Basic for all 7226 hours data, Premium for 945 hours subset.

text_extractor="pypinyin_initials_finals"  # default is espeak for English
audio_extractor="Encodec"  # or Fbank
audio_feats_dir=data/tokenized

. shared/parse_options.sh || exit 1


# All files generated by this script are saved in "data".
# You can safely remove "data" and rerun this script to regenerate it.
mkdir -p data
log() {
  # This function is from espnet
  local fname=${BASH_SOURCE[1]##*/}
  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}

if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
  log "dl_dir: $dl_dir"
  log "Stage 0: Download data"
  huggingface-cli login
  huggingface-cli download --repo-type dataset --local-dir $dl_dir Wenetspeech4TTS/WenetSpeech4TTS

  # Extract the downloaded data:
  for folder in Standard Premium Basic; do
    for file in "$dl_dir/$folder"/*.tar.gz; do
      tar -xzvf "$file" -C "$dl_dir/$folder"
    done
  done
fi

if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
  log "Stage 1: Prepare wenetspeech4tts manifest"
  # We assume that you have downloaded the wenetspeech4tts corpus
  # to $dl_dir/wenetspeech4tts
  mkdir -p data/manifests
  if [ ! -e data/manifests/.wenetspeech4tts.done ]; then
    lhotse prepare wenetspeech4tts $dl_dir data/manifests --dataset-parts "${dataset_parts}"
    touch data/manifests/.wenetspeech4tts.done
  fi
fi


if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
  log "Stage 2: Tokenize/Fbank wenetspeech4tts"
  mkdir -p ${audio_feats_dir}
  if [ ! -e ${audio_feats_dir}/.wenetspeech4tts.tokenize.done ]; then
    python3 ./local/compute_neural_codec_and_prepare_text_tokens.py --dataset-parts "${dataset_parts}" \
        --text-extractor ${text_extractor} \
        --audio-extractor ${audio_extractor} \
        --batch-duration 2500 --prefix "wenetspeech4tts" \
        --src-dir "data/manifests" \
	--split 100 \
        --output-dir "${audio_feats_dir}/wenetspeech4tts_${dataset_parts}_split_100"
    cp ${audio_feats_dir}/wenetspeech4tts_${dataset_parts}_split_100/unique_text_tokens.k2symbols ${audio_feats_dir}
  fi
  touch ${audio_feats_dir}/.wenetspeech4tts.tokenize.done
fi

if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
  log "Stage 3: Combine features"
  if [ ! -f ${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz ]; then
    pieces=$(find ${audio_feats_dir}/wenetspeech4tts_${dataset_parts}_split_100 -name "*.jsonl.gz")
    lhotse combine $pieces ${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz
  fi
fi

if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
  log "Stage 4: Prepare wenetspeech4tts train/dev/test"
  if [ ! -e ${audio_feats_dir}/.wenetspeech4tts.train.done ]; then

    lhotse subset --first 400 \
        ${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz \
        ${audio_feats_dir}/cuts_dev.jsonl.gz

    lhotse subset --last 400 \
        ${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz \
        ${audio_feats_dir}/cuts_test.jsonl.gz

    lhotse copy \
      ${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz \
      ${audio_feats_dir}/cuts_train.jsonl.gz

    touch ${audio_feats_dir}/.wenetspeech4tts.train.done
  fi
  python3 ./local/display_manifest_statistics.py --manifest-dir ${audio_feats_dir}
fi

subset="Basic"
prefix="wenetspeech4tts"
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
  log "Stage 5: Generate fbank (used by ./f5-tts)"
  mkdir -p data/fbank
  if [ ! -e data/fbank/.${prefix}.done ]; then
    ./local/compute_mel_feat.py --dataset-parts $subset --split 100
    touch data/fbank/.${prefix}.done
  fi
fi

if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
  log "Stage 6: Split the ${prefix} cuts into train, valid and test sets (used by ./f5-tts)"
  if [ ! -f data/fbank/${prefix}_cuts_${subset}.jsonl.gz ]; then
    echo "Combining ${prefix} cuts"
    pieces=$(find data/fbank/ -name "${prefix}_cuts_${subset}.*.jsonl.gz")
    lhotse combine $pieces data/fbank/${prefix}_cuts_${subset}.jsonl.gz
  fi
  if [ ! -e data/fbank/.${prefix}_split.done ]; then
    echo "Splitting ${prefix} cuts into train, valid and test sets"

    lhotse subset --last 800 \
      data/fbank/${prefix}_cuts_${subset}.jsonl.gz \
      data/fbank/${prefix}_cuts_validtest.jsonl.gz
    lhotse subset --first 400 \
      data/fbank/${prefix}_cuts_validtest.jsonl.gz \
      data/fbank/${prefix}_cuts_valid.jsonl.gz
    lhotse subset --last 400 \
      data/fbank/${prefix}_cuts_validtest.jsonl.gz \
      data/fbank/${prefix}_cuts_test.jsonl.gz

    rm data/fbank/${prefix}_cuts_validtest.jsonl.gz

    n=$(( $(gunzip -c data/fbank/${prefix}_cuts_${subset}.jsonl.gz | wc -l) - 800 ))
    lhotse subset --first $n  \
      data/fbank/${prefix}_cuts_${subset}.jsonl.gz \
      data/fbank/${prefix}_cuts_train.jsonl.gz
      touch data/fbank/.${prefix}_split.done
  fi
fi

if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
  log "Stage 7: Extract cosyvoice2 FSQ token (used by ./f5-tts semantic token experiment)"
  split_name=("valid" "test" "train")
  for split in "${split_name[@]}"; do
      echo "Processing $split"
      wav_scp_file=wav_${split}.scp
      output_dir="./cosy_v2_tokens_${split}"
      oringinal_jsonl_file=data/fbank/${prefix}_cuts_${split}.jsonl.gz
      mkdir -p $output_dir
      zcat $oringinal_jsonl_file | jq -r '.recording.id + " " + .recording.sources[0].source' > $wav_scp_file
      torchrun --nproc_per_node=8 --nnodes=1 \
          --rdzv_id=2024 --rdzv_backend="c10d" --rdzv_endpoint="localhost:0" \
          `which s3tokenizer` --wav_scp $wav_scp_file \
                      --device "cuda" \
                      --output_dir $output_dir \
                      --batch_size 32 \
                      --num_workers 4 \
                      --model "speech_tokenizer_v2_25hz"  # or "speech_tokenizer_v1_25hz

      cat $output_dir/* > $output_dir/${prefix}_${split}_cosy_v2_tokens.json
      python3 local/attach_speech_tokens.py --jsonl-prefix ${prefix}_cuts_${split} --tokens-path $output_dir/${prefix}_${split}_cosy_v2_tokens.json --manifest-dir data/fbank
  done
fi