Hugging Face's logo

Spaces:
cocktailpeanut
/
E2-F5-TTS
Runtime error

App Files Community
1
E2-F5-TTS
File size: 5,194 Bytes 
1646c30
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
 
import os
import re

import torch
import torchaudio
from einops import rearrange
from ema_pytorch import EMA
from vocos import Vocos

from model import CFM, UNetT, DiT, MMDiT
from model.utils import (
    get_tokenizer, 
    convert_char_to_pinyin, 
    save_spectrogram,
)

device = "cuda" if torch.cuda.is_available() else "cpu"


# --------------------- Dataset Settings -------------------- #

target_sample_rate = 24000
n_mel_channels = 100
hop_length = 256
target_rms = 0.1

tokenizer = "pinyin"
dataset_name = "Emilia_ZH_EN"


# ---------------------- infer setting ---------------------- #

seed = None  # int | None

exp_name = "F5TTS_Base"  # F5TTS_Base | E2TTS_Base
ckpt_step = 1200000

nfe_step = 32  # 16, 32
cfg_strength = 2.
ode_method = 'euler'  # euler | midpoint
sway_sampling_coef = -1.
speed = 1.
fix_duration = 27  # None (will linear estimate. if code-switched, consider fix) | float (total in seconds, include ref audio) 

if exp_name == "F5TTS_Base":
    model_cls = DiT
    model_cfg = dict(dim = 1024, depth = 22, heads = 16, ff_mult = 2, text_dim = 512, conv_layers = 4)

elif exp_name == "E2TTS_Base":
    model_cls = UNetT
    model_cfg = dict(dim = 1024, depth = 24, heads = 16, ff_mult = 4)

checkpoint = torch.load(f"ckpts/{exp_name}/model_{ckpt_step}.pt", map_location=device)
output_dir = "tests"

ref_audio = "tests/ref_audio/test_en_1_ref_short.wav"
ref_text = "Some call me nature, others call me mother nature."
gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences."

# ref_audio = "tests/ref_audio/test_zh_1_ref_short.wav"
# ref_text = "对，这就是我，万人敬仰的太乙真人。"
# gen_text = "突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道：\"我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？\""


# -------------------------------------------------#

use_ema = True

if not os.path.exists(output_dir):
    os.makedirs(output_dir)

# Vocoder model
local = False
if local:
    vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
    vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
    state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", map_location=device)
    vocos.load_state_dict(state_dict)
    vocos.eval()
else:
    vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")

# Tokenizer
vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)

# Model
model = CFM(
    transformer = model_cls(
        **model_cfg,
        text_num_embeds = vocab_size, 
        mel_dim = n_mel_channels
    ),
    mel_spec_kwargs = dict(
        target_sample_rate = target_sample_rate, 
        n_mel_channels = n_mel_channels,
        hop_length = hop_length,
    ),
    odeint_kwargs = dict(
        method = ode_method,
    ),
    vocab_char_map = vocab_char_map,
).to(device)

if use_ema == True:
    ema_model = EMA(model, include_online_model = False).to(device)
    ema_model.load_state_dict(checkpoint['ema_model_state_dict'])
    ema_model.copy_params_from_ema_to_model()
else:
    model.load_state_dict(checkpoint['model_state_dict'])

# Audio
audio, sr = torchaudio.load(ref_audio)
rms = torch.sqrt(torch.mean(torch.square(audio)))
if rms < target_rms:
    audio = audio * target_rms / rms
if sr != target_sample_rate:
    resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
    audio = resampler(audio)
audio = audio.to(device)

# Text
text_list = [ref_text + gen_text]
if tokenizer == "pinyin":
    final_text_list = convert_char_to_pinyin(text_list)
else:
    final_text_list = [text_list]
print(f"text  : {text_list}")
print(f"pinyin: {final_text_list}")

# Duration
ref_audio_len = audio.shape[-1] // hop_length
if fix_duration is not None:
    duration = int(fix_duration * target_sample_rate / hop_length)
else:  # simple linear scale calcul
    zh_pause_punc = r"。，、；：？！"
    ref_text_len = len(ref_text) + len(re.findall(zh_pause_punc, ref_text))
    gen_text_len = len(gen_text) + len(re.findall(zh_pause_punc, gen_text))
    duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)

# Inference
with torch.inference_mode():
    generated, trajectory = model.sample(
        cond = audio,
        text = final_text_list,
        duration = duration,
        steps = nfe_step,
        cfg_strength = cfg_strength,
        sway_sampling_coef = sway_sampling_coef,
        seed = seed,
    )
print(f"Generated mel: {generated.shape}")

# Final result
generated = generated[:, ref_audio_len:, :]
generated_mel_spec = rearrange(generated, '1 n d -> 1 d n')
generated_wave = vocos.decode(generated_mel_spec.cpu())
if rms < target_rms:
    generated_wave = generated_wave * rms / target_rms

save_spectrogram(generated_mel_spec[0].cpu().numpy(), f"{output_dir}/test_single.png")
torchaudio.save(f"{output_dir}/test_single.wav", generated_wave, target_sample_rate)
print(f"Generated wav: {generated_wave.shape}")