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use piper_phonemize as text tokenizer in ljspeech recipe

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yaozengwei 2024-02-20 17:45:56 +08:00
parent ff6784d147
commit 2cf5891c15
4 changed files with 66 additions and 64 deletions
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65
egs/ljspeech/TTS/local/prepare_token_file.py
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@ -17,7 +17,7 @@
"""
This file reads the texts in given manifest and generates the file that maps tokens to IDs.
This file generates the file that maps tokens to IDs.
"""
import argparse
@ -25,80 +25,47 @@ import logging
from pathlib import Path
from typing import Dict
from lhotse import load_manifest
from piper_phonemize import get_espeak_map
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--manifest-file",
type=Path,
default=Path("data/spectrogram/ljspeech_cuts_train.jsonl.gz"),
help="Path to the manifest file",
)
parser.add_argument(
"--tokens",
type=Path,
default=Path("data/tokens.txt"),
help="Path to the tokens",
help="Path to the dict that maps the text tokens to IDs",
)
return parser.parse_args()
def write_mapping(filename: str, sym2id: Dict[str, int]) -> None:
"""Write a symbol to ID mapping to a file.
Note:
No need to implement `read_mapping` as it can be done
through :func:`k2.SymbolTable.from_file`.
Args:
filename:
Filename to save the mapping.
sym2id:
A dict mapping symbols to IDs.
Returns:
Return None.
"""
with open(filename, "w", encoding="utf-8") as f:
for sym, i in sym2id.items():
f.write(f"{sym} {i}\n")
def get_token2id(manifest_file: Path) -> Dict[str, int]:
"""Return a dict that maps token to IDs."""
def get_token2id(filename: Path) -> Dict[str, int]:
"""Get a dict that maps token to IDs, and save it to the given filename."""
extra_tokens = [
"<blk>", # 0 for blank
"<sos/eos>", # 1 for sos and eos symbols.
"<unk>", # 2 for OOV
"<sos>", # 1 for sos
"<eos>", # 2 for eos
"<unk>", # 3 for OOV
]
all_tokens = set()
cut_set = load_manifest(manifest_file)
all_tokens = list(get_espeak_map().keys())
for cut in cut_set:
# Each cut only contain one supervision
assert len(cut.supervisions) == 1, len(cut.supervisions)
for t in cut.tokens:
all_tokens.add(t)
for t in extra_tokens:
assert t not in all_tokens, t
all_tokens = extra_tokens + list(all_tokens)
all_tokens = extra_tokens + all_tokens
token2id: Dict[str, int] = {token: i for i, token in enumerate(all_tokens)}
return token2id
with open(filename, "w", encoding="utf-8") as f:
for i, token in enumerate(all_tokens):
f.write(f"{token} {i}\n")
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
manifest_file = Path(args.manifest_file)
out_file = Path(args.tokens)
token2id = get_token2id(manifest_file)
write_mapping(out_file, token2id)
get_token2id(out_file)

9
egs/ljspeech/TTS/local/prepare_tokens_ljspeech.py
View File

@ -23,9 +23,9 @@ This file reads the texts in given manifest and save the new cuts with phoneme t
import logging
from pathlib import Path
import g2p_en
import tacotron_cleaner.cleaners
from lhotse import CutSet, load_manifest
from piper_phonemize import phonemize_espeak
def prepare_tokens_ljspeech():
@ -35,7 +35,6 @@ def prepare_tokens_ljspeech():
partition = "all"
cut_set = load_manifest(output_dir / f"{prefix}_cuts_{partition}.{suffix}")
g2p = g2p_en.G2p()
new_cuts = []
for cut in cut_set:
@ -45,7 +44,11 @@ def prepare_tokens_ljspeech():
# Text normalization
text = tacotron_cleaner.cleaners.custom_english_cleaners(text)
# Convert to phonemes
cut.tokens = g2p(text)
tokens_list = phonemize_espeak(text, "en-us")
tokens = []
for t in tokens_list:
tokens.extend(t)
cut.tokens = tokens
new_cuts.append(cut)
new_cut_set = CutSet.from_cuts(new_cuts)

14
egs/ljspeech/TTS/prepare.sh
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@ -30,7 +30,7 @@ if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
cd vits/monotonic_align
python setup.py build_ext --inplace
cd ../../
else
else
log "monotonic_align lib already built"
fi
fi
@ -80,6 +80,10 @@ fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Prepare phoneme tokens for LJSpeech"
# We assume you have installed piper_phonemize and espnet_tts_frontend.
# If not, please install them with:
# - piper_phonemize: refer to https://github.com/rhasspy/piper-phonemize
# - espnet_tts_frontend, `pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/
if [ ! -e data/spectrogram/.ljspeech_with_token.done ]; then
./local/prepare_tokens_ljspeech.py
mv data/spectrogram/ljspeech_cuts_with_tokens_all.jsonl.gz \
@ -113,13 +117,11 @@ fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Generate token file"
# We assume you have installed g2p_en and espnet_tts_frontend.
# We assume you have installed piper_phonemize and espnet_tts_frontend.
# If not, please install them with:
# - g2p_en: `pip install g2p_en`, refer to https://github.com/Kyubyong/g2p
# - piper_phonemize: refer to https://github.com/rhasspy/piper-phonemize
# - espnet_tts_frontend, `pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/
if [ ! -e data/tokens.txt ]; then
./local/prepare_token_file.py \
--manifest-file data/spectrogram/ljspeech_cuts_train.jsonl.gz \
--tokens data/tokens.txt
./local/prepare_token_file.py --tokens data/tokens.txt
fi
fi

42
egs/ljspeech/TTS/vits/tokenizer.py
View File

@ -16,8 +16,8 @@
from typing import Dict, List
import g2p_en
import tacotron_cleaner.cleaners
from piper_phonemize import phonemize_espeak
from utils import intersperse
@ -41,18 +41,28 @@ class Tokenizer(object):
self.token2id[token] = id
self.blank_id = self.token2id["<blk>"]
self.sos_id = self.token2id["<sos>"]
self.eos_id = self.token2id["<eos>"]
self.oov_id = self.token2id["<unk>"]
self.vocab_size = len(self.token2id)
self.g2p = g2p_en.G2p()
def texts_to_token_ids(self, texts: List[str], intersperse_blank: bool = True):
def texts_to_token_ids(
self,
texts: List[str],
intersperse_blank: bool = True,
add_sos: bool = False,
add_eos: bool = False,
):
"""
Args:
texts:
A list of transcripts.
intersperse_blank:
Whether to intersperse blanks in the token sequence.
add_sos:
Whether to add sos token at the start.
add_eos:
Whether to add eos token at the end.
Returns:
Return a list of token id list [utterance][token_id]
@ -63,7 +73,11 @@ class Tokenizer(object):
# Text normalization
text = tacotron_cleaner.cleaners.custom_english_cleaners(text)
# Convert to phonemes
tokens = self.g2p(text)
tokens_list = phonemize_espeak(text, "en-us")
tokens = []
for t in tokens_list:
tokens.extend(t)
token_ids = []
for t in tokens:
if t in self.token2id:
@ -73,13 +87,21 @@ class Tokenizer(object):
if intersperse_blank:
token_ids = intersperse(token_ids, self.blank_id)
if add_sos:
token_ids = [self.sos_id] + token_ids
if add_eos:
token_ids = token_ids + [self.eos_id]
token_ids_list.append(token_ids)
return token_ids_list
def tokens_to_token_ids(
self, tokens_list: List[str], intersperse_blank: bool = True
self,
tokens_list: List[str],
intersperse_blank: bool = True,
add_sos: bool = False,
add_eos: bool = False,
):
"""
Args:
@ -87,6 +109,10 @@ class Tokenizer(object):
A list of token list, each corresponding to one utterance.
intersperse_blank:
Whether to intersperse blanks in the token sequence.
add_sos:
Whether to add sos token at the start.
add_eos:
Whether to add eos token at the end.
Returns:
Return a list of token id list [utterance][token_id]
@ -103,6 +129,10 @@ class Tokenizer(object):
if intersperse_blank:
token_ids = intersperse(token_ids, self.blank_id)
if add_sos:
token_ids = [self.sos_id] + token_ids
if add_eos:
token_ids = token_ids + [self.eos_id]
token_ids_list.append(token_ids)