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on
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Running
on
T4
File size: 2,516 Bytes
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import os
import re
import subprocess
import tempfile
import librosa
import torch
def normalize_text(text: str) -> str:
unicode_conversion = {
8175: "'",
8189: "'",
8190: "'",
8208: "-",
8209: "-",
8210: "-",
8211: "-",
8212: "-",
8213: "-",
8214: "||",
8216: "'",
8217: "'",
8218: ",",
8219: "`",
8220: '"',
8221: '"',
8222: ",,",
8223: '"',
8228: ".",
8229: "..",
8230: "...",
8242: "'",
8243: '"',
8245: "'",
8246: '"',
180: "'",
2122: "TM", # Trademark
}
text = text.translate(unicode_conversion)
non_bpe_chars = set([c for c in list(text) if ord(c) >= 256])
#if len(non_bpe_chars) > 0:
# non_bpe_points = [(c, ord(c)) for c in non_bpe_chars]
# raise ValueError(f"Non-BPE single token characters found: {non_bpe_points}")
text = text.replace("\t", " ")
text = text.replace("\n", " ")
text = text.replace("*", " ")
text = text.strip()
text = re.sub("\s\s+", " ", text) # remove multiple spaces
return text
def check_audio_file(path_or_uri, threshold_s=10): # default 30
if "http" in path_or_uri:
temp_fd, filepath = tempfile.mkstemp()
os.close(temp_fd) # Close the file descriptor, curl will create a new connection
curl_command = ["curl", "-L", path_or_uri, "-o", filepath]
subprocess.run(curl_command, check=True)
else:
filepath = path_or_uri
audio, sr = librosa.load(filepath)
duration_s = librosa.get_duration(y=audio, sr=sr)
if duration_s < threshold_s:
raise Exception(
f"The audio file is too short. Please provide an audio file that is at least {threshold_s} seconds long to proceed."
)
# Clean up the temporary file if it was created
if "http" in path_or_uri:
os.remove(filepath)
def get_default_dtype() -> str:
"""Compute default 'dtype' based on GPU architecture"""
if torch.cuda.is_available():
for i in range(torch.cuda.device_count()):
device_properties = torch.cuda.get_device_properties(i)
dtype = "float16" if device_properties.major <= 7 else "bfloat16" # tesla and turing architectures
else:
dtype = "float16"
print(f"using dtype={dtype}")
return dtype
def get_device() -> str:
return "cuda" if torch.cuda.is_available() else "cpu"
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