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Adding matcha vctk

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Shivam Mehta
2023-09-20 07:08:11 +00:00
parent 9ceee279f0
commit 72635012b0
1 changed files with 71 additions and 21 deletions
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92
matcha/cli.py
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@@ -18,17 +18,21 @@ from matcha.text import sequence_to_text, text_to_sequence
from matcha.utils.utils import assert_model_downloaded, get_user_data_dir, intersperse
MATCHA_URLS = {
"matcha_ljspeech": "https://drive.google.com/file/d/1BBzmMU7k3a_WetDfaFblMoN18GqQeHCg/view?usp=drive_link"
} # , "matcha_vctk": ""} # Coming soon
MULTISPEAKER_MODEL = {"matcha_vctk"}
SINGLESPEAKER_MODEL = {"matcha_ljspeech"}
"matcha_ljspeech": "https://drive.google.com/file/d/1BBzmMU7k3a_WetDfaFblMoN18GqQeHCg/view?usp=drive_link",
"matcha_vctk": "", # Coming soon
}
VOCODER_URLS = {
"hifigan_T2_v1": "https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link",
"hifigan_univ_v1": "https://drive.google.com/file/d/1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW/view?usp=drive_link",
}
MULTISPEAKER_MODEL = {
"matcha_vctk": {"vocoder": "hifigan_univ_v1", "speaking_rate": 0.85, "spk": 0, "spk_range": (0, 108)}
}
SINGLESPEAKER_MODEL = {"matcha_ljspeech": {"vocoder": "hifigan_T2_v1", "speaking_rate": 0.95, "spk": None}}
def plot_spectrogram_to_numpy(spectrogram, filename):
fig, ax = plt.subplots(figsize=(12, 3))
@@ -132,28 +136,70 @@ def validate_args(args):
args.text or args.file
), "Either text or file must be provided Matcha-T(ea)TTS need sometext to whisk the waveforms."
assert args.temperature >= 0, "Sampling temperature cannot be negative"
assert args.speaking_rate > 0, "Speaking rate must be greater than 0"
assert args.steps > 0, "Number of ODE steps must be greater than 0"
if args.checkpoint_path is None:
if args.model in SINGLESPEAKER_MODEL:
assert args.spk is None, f"Speaker ID is not supported for {args.model}"
if args.spk is not None:
assert args.spk >= 0 and args.spk < 109, "Speaker ID must be between 0 and 108"
assert args.model in MULTISPEAKER_MODEL, "Speaker ID is only supported for multispeaker model"
if args.checkpoint_path is None:
# When using pretrained models
if args.model in SINGLESPEAKER_MODEL.keys():
args = validate_args_for_single_speaker_model(args)
if args.model in MULTISPEAKER_MODEL:
if args.spk is None:
print("[!] Speaker ID not provided! Using speaker ID 0")
args.spk = 0
args.vocoder = "hifigan_univ_v1"
args = validate_args_for_multispeaker_model(args)
else:
# When using a custom model
if args.vocoder != "hifigan_univ_v1":
warn_ = "[-] Using custom model checkpoint! I would suggest passing --vocoder hifigan_univ_v1, unless the custom model is trained on LJ Speech."
warnings.warn(warn_, UserWarning)
if args.speaking_rate is None:
args.speaking_rate = 1.0
if args.batched:
assert args.batch_size > 0, "Batch size must be greater than 0"
assert args.speaking_rate > 0, "Speaking rate must be greater than 0"
return args
def validate_args_for_multispeaker_model(args):
if args.vocoder is not None:
if args.vocoder != MULTISPEAKER_MODEL[args.model]["vocoder"]:
warn_ = f"[-] Using {args.model} model! I would suggest passing --vocoder {MULTISPEAKER_MODEL[args.model]['vocoder']}"
warnings.warn(warn_, UserWarning)
else:
args.vocoder = MULTISPEAKER_MODEL[args.model]["vocoder"]
if args.speaking_rate is None:
args.speaking_rate = MULTISPEAKER_MODEL[args.model]["speaking_rate"]
spk_range = MULTISPEAKER_MODEL[args.model]["spk_range"]
if args.spk is not None:
assert (
args.spk >= spk_range[0] and args.spk <= spk_range[-1]
), f"Speaker ID must be between {spk_range} for this model."
else:
available_spk_id = MULTISPEAKER_MODEL[args.model]["spk"]
warn_ = f"[!] Speaker ID not provided! Using speaker ID {available_spk_id}"
warnings.warn(warn_, UserWarning)
args.spk = available_spk_id
return args
def validate_args_for_single_speaker_model(args):
if args.vocoder is not None:
if args.vocoder != SINGLESPEAKER_MODEL[args.model]["vocoder"]:
warn_ = f"[-] Using {args.model} model! I would suggest passing --vocoder {SINGLESPEAKER_MODEL[args.model]['vocoder']}"
warnings.warn(warn_, UserWarning)
else:
args.vocoder = SINGLESPEAKER_MODEL[args.model]["vocoder"]
if args.speaking_rate is None:
args.speaking_rate = SINGLESPEAKER_MODEL[args.model]["speaking_rate"]
if args.spk != SINGLESPEAKER_MODEL[args.model]["spk"]:
warn_ = f"[-] Ignoring speaker id {args.spk} for {args.model}"
warnings.warn(warn_, UserWarning)
args.spk = SINGLESPEAKER_MODEL[args.model]["spk"]
return args
@@ -181,8 +227,8 @@ def cli():
parser.add_argument(
"--vocoder",
type=str,
default="hifigan_T2_v1",
help="Vocoder to use",
default=None,
help="Vocoder to use (default: will use the one suggested with the pretrained model))",
choices=VOCODER_URLS.keys(),
)
parser.add_argument("--text", type=str, default=None, help="Text to synthesize")
@@ -197,7 +243,7 @@ def cli():
parser.add_argument(
"--speaking_rate",
type=float,
default=1.0,
default=None,
help="change the speaking rate, a higher value means slower speaking rate (default: 1.0)",
)
parser.add_argument("--steps", type=int, default=10, help="Number of ODE steps (default: 10)")
@@ -214,8 +260,10 @@ def cli():
default=os.getcwd(),
help="Output folder to save results (default: current dir)",
)
parser.add_argument("--batched", action="store_true")
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--batched", action="store_true", help="Batched inference (default: False)")
parser.add_argument(
"--batch_size", type=int, default=32, help="Batch size only useful when --batched (default: 32)"
)
args = parser.parse_args()
@@ -348,6 +396,8 @@ def unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk):
def print_config(args):
print("[!] Configurations: ")
print(f"\t- Model: {args.model}")
print(f"\t- Vocoder: {args.vocoder}")
print(f"\t- Temperature: {args.temperature}")
print(f"\t- Speaking rate: {args.speaking_rate}")
print(f"\t- Number of ODE steps: {args.steps}")