Medix-AI/coqui-tts
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coqui-tts
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Merge pull request #3 from eginhard/remove-pandas 

Remove pandas
This commit is contained in:
Enno Hermann 2024-03-06 22:07:32 +01:00 committed by GitHub
parent 24298da5fc e4b1b0f73e
commit f24f7c1237
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GPG Key ID: B5690EEEBB952194
4 changed files with 34 additions and 28 deletions
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9
TTS/encoder/utils/prepare_voxceleb.py
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@ -19,13 +19,13 @@
# pylint: disable=too-many-locals, too-many-statements, too-many-arguments, too-many-instance-attributes
""" voxceleb 1 & 2 """
import csv
import hashlib
import os
import subprocess
import sys
import zipfile
import pandas
import soundfile as sf
from absl import logging
@ -185,8 +185,11 @@ def convert_audio_and_make_label(input_dir, subset, output_dir, output_file):
# Write to CSV file which contains four columns:
# "wav_filename", "wav_length_ms", "speaker_id", "speaker_name".
csv_file_path = os.path.join(output_dir, output_file)
df = pandas.DataFrame(data=files, columns=["wav_filename", "wav_length_ms", "speaker_id", "speaker_name"])
df.to_csv(csv_file_path, index=False, sep="\t")
with open(csv_file_path, "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f, delimiter="\t")
writer.writerow(["wav_filename", "wav_length_ms", "speaker_id", "speaker_name"])
for wav_file in files:
writer.writerow(wav_file)
logging.info("Successfully generated csv file {}".format(csv_file_path))

46
TTS/tts/datasets/formatters.py
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@ -1,3 +1,4 @@
import csv
import os
import re
import xml.etree.ElementTree as ET
@ -5,7 +6,6 @@ from glob import glob
from pathlib import Path
from typing import List
import pandas as pd
from tqdm import tqdm
########################
@ -25,25 +25,27 @@ def cml_tts(root_path, meta_file, ignored_speakers=None):
if len(line.split("|")) != num_cols:
print(f" > Missing column in line {idx + 1} -> {line.strip()}")
# load metadata
metadata = pd.read_csv(os.path.join(root_path, meta_file), sep="|")
assert all(x in metadata.columns for x in ["wav_filename", "transcript"])
client_id = None if "client_id" in metadata.columns else "default"
emotion_name = None if "emotion_name" in metadata.columns else "neutral"
with open(Path(root_path) / meta_file, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="|")
metadata = list(reader)
assert all(x in metadata[0] for x in ["wav_filename", "transcript"])
client_id = None if "client_id" in metadata[0] else "default"
emotion_name = None if "emotion_name" in metadata[0] else "neutral"
items = []
not_found_counter = 0
for row in metadata.itertuples():
if client_id is None and ignored_speakers is not None and row.client_id in ignored_speakers:
for row in metadata:
if client_id is None and ignored_speakers is not None and row["client_id"] in ignored_speakers:
continue
audio_path = os.path.join(root_path, row.wav_filename)
audio_path = os.path.join(root_path, row["wav_filename"])
if not os.path.exists(audio_path):
not_found_counter += 1
continue
items.append(
{
"text": row.transcript,
"text": row["transcript"],
"audio_file": audio_path,
"speaker_name": client_id if client_id is not None else row.client_id,
"emotion_name": emotion_name if emotion_name is not None else row.emotion_name,
"speaker_name": client_id if client_id is not None else row["client_id"],
"emotion_name": emotion_name if emotion_name is not None else row["emotion_name"],
"root_path": root_path,
}
)
@ -63,25 +65,27 @@ def coqui(root_path, meta_file, ignored_speakers=None):
if len(line.split("|")) != num_cols:
print(f" > Missing column in line {idx + 1} -> {line.strip()}")
# load metadata
metadata = pd.read_csv(os.path.join(root_path, meta_file), sep="|")
assert all(x in metadata.columns for x in ["audio_file", "text"])
speaker_name = None if "speaker_name" in metadata.columns else "coqui"
emotion_name = None if "emotion_name" in metadata.columns else "neutral"
with open(Path(root_path) / meta_file, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="|")
metadata = list(reader)
assert all(x in metadata[0] for x in ["audio_file", "text"])
speaker_name = None if "speaker_name" in metadata[0] else "coqui"
emotion_name = None if "emotion_name" in metadata[0] else "neutral"
items = []
not_found_counter = 0
for row in metadata.itertuples():
if speaker_name is None and ignored_speakers is not None and row.speaker_name in ignored_speakers:
for row in metadata:
if speaker_name is None and ignored_speakers is not None and row["speaker_name"] in ignored_speakers:
continue
audio_path = os.path.join(root_path, row.audio_file)
audio_path = os.path.join(root_path, row["audio_file"])
if not os.path.exists(audio_path):
not_found_counter += 1
continue
items.append(
{
"text": row.text,
"text": row["text"],
"audio_file": audio_path,
"speaker_name": speaker_name if speaker_name is not None else row.speaker_name,
"emotion_name": emotion_name if emotion_name is not None else row.emotion_name,
"speaker_name": speaker_name if speaker_name is not None else row["speaker_name"],
"emotion_name": emotion_name if emotion_name is not None else row["emotion_name"],
"root_path": root_path,
}
)

3
requirements.notebooks.txt
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@ -1 +1,2 @@
bokeh==1.4.0
bokeh==1.4.0
pandas>=1.4,<2.0

4
requirements.txt
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@ -8,7 +8,6 @@ torchaudio
soundfile>=0.12.0
librosa>=0.10.0
scikit-learn>=1.3.0
numba==0.55.1;python_version<"3.9"
numba>=0.57.0;python_version>="3.9"
inflect>=5.6.0
tqdm>=4.64.1
@ -24,7 +23,6 @@ flask>=2.0.1
pysbd>=0.3.4
# deps for notebooks
umap-learn>=0.5.1
pandas>=1.4,<2.0
# deps for training
matplotlib>=3.7.0
# coqui stack
@ -54,4 +52,4 @@ encodec>=0.1.1
# deps for XTTS
unidecode>=1.3.2
num2words
spacy[ja]>=3
spacy[ja]>=3