mirror of
https://github.com/shivammehta25/Matcha-TTS.git
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Merge pull request #43 from shivammehta25/dev
Removing gdown for HifiGAN checkpoints too
This commit is contained in:
11
.pylintrc
11
.pylintrc
@@ -292,13 +292,6 @@ max-line-length=120
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# Maximum number of lines in a module.
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# Maximum number of lines in a module.
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max-module-lines=1000
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max-module-lines=1000
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# List of optional constructs for which whitespace checking is disabled. `dict-
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# separator` is used to allow tabulation in dicts, etc.: {1 : 1,\n222: 2}.
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# `trailing-comma` allows a space between comma and closing bracket: (a, ).
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# `empty-line` allows space-only lines.
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no-space-check=trailing-comma,
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dict-separator
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# Allow the body of a class to be on the same line as the declaration if body
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# Allow the body of a class to be on the same line as the declaration if body
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# contains single statement.
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# contains single statement.
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single-line-class-stmt=no
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single-line-class-stmt=no
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@@ -528,5 +521,5 @@ min-public-methods=2
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# Exceptions that will emit a warning when being caught. Defaults to
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# Exceptions that will emit a warning when being caught. Defaults to
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# "BaseException, Exception".
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# "BaseException, Exception".
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overgeneral-exceptions=BaseException,
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overgeneral-exceptions=builtins.BaseException,
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Exception
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builtins.Exception
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@@ -1 +1 @@
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0.0.5
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0.0.5.1
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@@ -29,8 +29,15 @@ args = Namespace(
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CURRENTLY_LOADED_MODEL = args.model
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CURRENTLY_LOADED_MODEL = args.model
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MATCHA_TTS_LOC = lambda x: LOCATION / f"{x}.ckpt" # noqa: E731
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VOCODER_LOC = lambda x: LOCATION / f"{x}" # noqa: E731
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def MATCHA_TTS_LOC(x):
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return LOCATION / f"{x}.ckpt"
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def VOCODER_LOC(x):
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return LOCATION / f"{x}"
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LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png"
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LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png"
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RADIO_OPTIONS = {
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RADIO_OPTIONS = {
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"Multi Speaker (VCTK)": {
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"Multi Speaker (VCTK)": {
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@@ -23,8 +23,8 @@ MATCHA_URLS = {
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}
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}
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VOCODER_URLS = {
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VOCODER_URLS = {
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"hifigan_T2_v1": "https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link",
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"hifigan_T2_v1": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/generator_v1", # Old url: https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link
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"hifigan_univ_v1": "https://drive.google.com/file/d/1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW/view?usp=drive_link",
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"hifigan_univ_v1": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/g_02500000", # Old url: https://drive.google.com/file/d/1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW/view?usp=drive_link
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}
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}
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MULTISPEAKER_MODEL = {
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MULTISPEAKER_MODEL = {
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@@ -115,7 +115,7 @@ def get_metric_value(metric_dict: Dict[str, Any], metric_name: str) -> float:
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return None
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return None
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if metric_name not in metric_dict:
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if metric_name not in metric_dict:
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raise Exception(
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raise ValueError(
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f"Metric value not found! <metric_name={metric_name}>\n"
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f"Metric value not found! <metric_name={metric_name}>\n"
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"Make sure metric name logged in LightningModule is correct!\n"
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"Make sure metric name logged in LightningModule is correct!\n"
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"Make sure `optimized_metric` name in `hparams_search` config is correct!"
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"Make sure `optimized_metric` name in `hparams_search` config is correct!"
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@@ -205,11 +205,13 @@ def get_user_data_dir(appname="matcha_tts"):
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return final_path
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return final_path
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def assert_model_downloaded(checkpoint_path, url, use_wget=False):
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def assert_model_downloaded(checkpoint_path, url, use_wget=True):
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if Path(checkpoint_path).exists():
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if Path(checkpoint_path).exists():
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log.debug(f"[+] Model already present at {checkpoint_path}!")
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log.debug(f"[+] Model already present at {checkpoint_path}!")
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print(f"[+] Model already present at {checkpoint_path}!")
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return
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return
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log.info(f"[-] Model not found at {checkpoint_path}! Will download it")
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log.info(f"[-] Model not found at {checkpoint_path}! Will download it")
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print(f"[-] Model not found at {checkpoint_path}! Will download it")
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checkpoint_path = str(checkpoint_path)
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checkpoint_path = str(checkpoint_path)
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if not use_wget:
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if not use_wget:
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gdown.download(url=url, output=checkpoint_path, quiet=False, fuzzy=True)
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gdown.download(url=url, output=checkpoint_path, quiet=False, fuzzy=True)
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