Spaces:
Running
Running
| 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" | |