diff --git a/vall_e/config.py b/vall_e/config.py
index 640859d..267eb18 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -311,8 +311,8 @@ class DeepSpeed:
"quantization_period": 0
},
"modules": [
- "blocks",
- "retnet",
+ "blocks", # for transformer-based models
+ "retnet", # for RetNets-based models
]
}
}
diff --git a/vall_e/data.py b/vall_e/data.py
index 9bb507c..f87ad91 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -10,6 +10,7 @@ import random
import torch
from .config import cfg
+from .emb.qnt import trim_random, repeat_extend_audio, merge_audio
from collections import defaultdict
from functools import cache, cached_property
@@ -33,6 +34,20 @@ def get_phone_symmap():
symmap = {'': 1, '': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
return symmap
+def get_task_symmap():
+ start = 1024
+ symmap = {
+ "": -100,
+ "": start + 0,
+ "": start + 1,
+ "": start + 2,
+ "": start + 3,
+ "": start + 4,
+ "": start + 5,
+ "": start + 6,
+ }
+ return symmap
+
def _replace_file_extension(path, suffix):
return (path.parent / path.name.split(".")[0]).with_suffix(suffix)
@@ -112,6 +127,7 @@ class Dataset(_Dataset):
paths,
phone_symmap=None,
spkr_symmap=None,
+ task_symmap=None,
min_phones=cfg.dataset.phones_range[0],
max_phones=cfg.dataset.phones_range[1],
min_duration=cfg.dataset.duration_range[0],
@@ -134,8 +150,9 @@ class Dataset(_Dataset):
else:
self.paths = paths
- self.spkr_symmap = spkr_symmap or self._get_spkr_symmap()
self.phone_symmap = phone_symmap or self._get_phone_symmap()
+ self.spkr_symmap = spkr_symmap or self._get_spkr_symmap()
+ self.task_symmap = get_task_symmap or self._get_task_symmap()
self.training = training
# assert len(self.phone_symmap) < 256, "Unique token count should be [0,255] to fit within uint8"
@@ -169,6 +186,7 @@ class Dataset(_Dataset):
self.durations[spkr_id] = duration
else:
self.durations[spkr_id] += duration
+
def _get_paths_by_spkr_name(self, extra_paths_by_spkr_name: dict[str, list]):
ret = defaultdict(list)
for path in self.paths:
@@ -181,16 +199,29 @@ class Dataset(_Dataset):
def phones(self):
return sorted(set().union(*[_get_phones(path) for path in self.paths]))
- def _get_phone_symmap(self):
- return get_phone_symmap()
-
@cached_property
def spkrs(self):
return sorted({cfg.get_spkr(path) for path in self.paths})
+ @cached_property
+ def tasks(self):
+ return ["tts"] # "ns", "sr", "tse", "cse", "nse"
+
+ def _get_phone_symmap(self):
+ return get_phone_symmap()
+
def _get_spkr_symmap(self):
return {s: i for i, s in enumerate(self.spkrs)}
+ def _get_task_symmap(self):
+ return get_task_symmap()
+
+ def get_task_token( token ):
+ return torch.Tensor([[ self.tasks_symmap[f'<{token}>'] for _ in range(len(cfg.models.prom_levels)) ]], dtype=torch.int16)
+
+ def sample_noise(self):
+ ...
+
def sample_speakers(self, ignore=[]):
choices = set(self.spkrs) - set(ignore)
return random.choice([*choices])
@@ -212,17 +243,7 @@ class Dataset(_Dataset):
# shuffle it up a bit
offset = random.randint(-16, 16)
- trim_length = int(cfg.dataset.prompt_duration * 75) + offset
- def trim( qnt ):
- length = qnt.shape[0]
- start = int(length * random.random())
- end = start + trim_length
- if end >= length:
- start = length - trim_length
- end = length
-
- return qnt[start:end]
-
+ trim_length = int(cfg.dataset.prompt_duration * 75) + offset
total_qnt_length = 0
for _ in range(cfg.dataset.max_prompts):
path = random.choice(choices)
@@ -234,7 +255,7 @@ class Dataset(_Dataset):
qnt = _load_quants(path)
if cfg.dataset.prompt_duration > 0 and trim_length < qnt.shape[0]:
- qnt = trim(qnt)
+ qnt = trim_random( qnt, trim_length )
prom_list.append(qnt)
total_qnt_length += qnt.shape[0]
@@ -248,14 +269,10 @@ class Dataset(_Dataset):
prom = torch.cat(prom_list)
if cfg.dataset.prompt_duration > 0 and trim_length < prom.shape[0]:
- prom = trim(prom)
+ prom = trim_random( prom, trim_length )
return prom
- @cached_property
- def tasks(self):
- return ["tts"] # "ns", "sr", "tse", "cse", "nse"
-
def __getitem__(self, index):
if cfg.dataset.sample_type == "speaker":
spkr_name = self.spkrs[index]
@@ -275,34 +292,45 @@ class Dataset(_Dataset):
resps = _load_quants(path)
task = random.choice(self.tasks)
+
# text-to-speech
if task == "tts":
proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
- # noise-suppression
+
"""
- elif task == "ns":
- proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
+ # noise suppression || speech removal
+ elif task == "ns" or task == "sr":
+ # sample random noise
noise = self.sample_noise()
- noise = extend_audio(noise, proms.shape[0])
- proms = merge_audio(proms, noise)
- # something to prepend a ns token to the beginning of proms
- elif task == "sr":
- proms = resps
- resps = self.sample_noise()
- resps = extend_audio(resps, proms.shape[0])
- # something to prepend a sr token to the beginning of proms
+ # extend the noise to fill the target audio
+ noise = repeat_extend_audio(noise, resps.shape[0])
+ # create the input prompt by merging the target audio with the noise
+ proms = merge_audio(resps, noise)
+ # set the target to just be the noise if
+ if task == "sr":
+ resps = noise
+ # prepend the task token
+ proms = torch.cat( [self.get_task_token(task), proms] )
+ # target speech extraction
elif task == "tse":
- proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
- other_speaker = self.sample_speaker(ignore=[spkr_name])
- other_proms = self.sample_prompts(other_speaker, ignore="")
- proms = merge_audio(proms, other_proms)
- # something to prepend a tse token to the beginning of proms
- """
+ # sample a random, clean, utterance for the target speaker
+ clean_proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
+ # sample a random, clean utterance from a different speaker
+ other_proms = self.sample_prompts(self.sample_speaker(ignore=[spkr_name]), ignore="")
+ # overlay the random speaker over the target audio
+ noisy_proms = merge_audio(resps, other_proms)
+ # stitch together the promps
+ proms = torch.cat( [clean_proms, self.get_task_token(task), noisy_proms] )
"""
# speech editing would require higher quality transcription data (phoneme level/word level) unfortunately
# as I need to get a good clean point to trim into
+ """
+ # clean speech editing
elif task == "cse":
+ ...
+ # noisy speech editing
elif task == "nse":
+ ...
"""
diff --git a/vall_e/emb/qnt.py b/vall_e/emb/qnt.py
index 4b425a5..0c74da6 100755
--- a/vall_e/emb/qnt.py
+++ b/vall_e/emb/qnt.py
@@ -128,7 +128,7 @@ def _replace_file_extension(path, suffix):
@torch.inference_mode()
-def encode(wav: Tensor, sr: int, device="cuda"):
+def encode(wav: Tensor, sr: int = 24_000, device="cuda"):
"""
Args:
wav: (t)
@@ -177,6 +177,36 @@ def encode_from_file(path, device="cuda"):
return qnt
+# Helper Functions
+
+# trims a random piece of audio, up to `target`
+def trim_random( qnt, target ):
+ length = qnt.shape[0]
+ start = int(length * random.random())
+ end = start + target
+ if end >= length:
+ start = length - target
+ end = length
+
+ return qnt[start:end]
+
+# repeats the audio to fit the target size
+def repeat_extend_audio( qnt, target ):
+ pieces = []
+ length = 0
+ while length < target:
+ pieces.append(qnt)
+ length += qnt.shape[0]
+
+ return trim_random(torch.cat(pieces), target)
+
+# merges two quantized audios together
+# I don't know if this works
+def merge_audio( *args, device="cpu" ):
+ qnts = [*args]
+ decoded = [ decode_to_wave(qnt, device=device)[0] for qnt in qnts ]
+ combined = sum(decoded) / len(decoded)
+ return encode(combined, 24_000, device="cpu")
def main():
parser = argparse.ArgumentParser()
diff --git a/vall_e/inference.py b/vall_e/inference.py
index 62371f6..e7f2ac4 100755
--- a/vall_e/inference.py
+++ b/vall_e/inference.py
@@ -5,6 +5,7 @@ import soundfile
from einops import rearrange
from .emb import g2p, qnt
+from .emb.qnt import trim_random
from .utils import to_device
from .config import cfg
@@ -12,17 +13,6 @@ from .models import get_models
from .train import load_engines
from .data import get_phone_symmap
-import random
-
-def trim( qnt, trim_length ):
- length = qnt.shape[0]
- start = int(length * random.random())
- end = start + trim_length
- if end >= length:
- start = length - trim_length
- end = length
- return qnt[start:end]
-
class TTS():
def __init__( self, config=None, ar_ckpt=None, nar_ckpt=None, device="cuda" ):
self.loading = True
@@ -91,7 +81,7 @@ class TTS():
enc = qnt.encode_from_file( path )
res = enc[0].t().to(torch.int16)
if trim:
- res = trim( res, int( 75 * cfg.dataset.duration_range[1] ) )
+ res = trim_random( res, int( 75 * cfg.dataset.duration_range[1] ) )
return res