File size: 14,573 Bytes
a674527
 
dd217c7
a674527
 
 
 
 
 
dd217c7
 
a674527
 
dd217c7
 
 
a674527
 
 
 
 
dd217c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0ac3155
 
 
 
 
 
 
 
 
 
dd217c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a674527
 
 
 
 
 
dd217c7
 
 
 
 
 
 
 
 
 
a674527
 
 
 
 
dd217c7
 
 
 
 
 
 
a674527
 
 
dd217c7
 
0ac3155
 
dd217c7
 
 
a674527
dd217c7
 
 
 
 
 
a674527
 
 
 
 
 
 
 
d9c8497
a674527
dd217c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0ac3155
 
 
 
 
 
 
 
1d03890
 
 
0ac3155
 
dd217c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8474faf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a674527
 
 
 
 
 
 
 
 
 
 
 
 
dd217c7
 
a674527
 
dd217c7
a674527
 
 
dd217c7
a674527
 
 
 
 
0ac3155
 
 
a674527
8474faf
dd217c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
364456d
 
dd217c7
 
 
 
 
 
 
4064aae
 
dd217c7
 
 
 
 
 
 
 
 
 
 
 
 
1d03890
dd217c7
 
 
 
 
 
 
 
 
 
 
 
a674527
 
 
 
 
 
 
 
 
dd217c7
a674527
 
 
dd217c7
a674527
 
 
 
dd217c7
a674527
 
 
dd217c7
a674527
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dd217c7
a674527
 
 
d9c8497
dd217c7
 
 
a674527
dd217c7
 
 
 
 
 
 
 
 
 
 
 
 
 
8474faf
dd217c7
 
 
 
 
 
 
 
 
a674527
 
8474faf
a674527
 
 
 
 
 
dd217c7
 
 
a674527
 
dd217c7
 
 
 
8474faf
dd217c7
 
8474faf
a674527
 
 
 
 
 
 
 
d9c8497
 
 
a674527
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8474faf
a674527
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0ac3155
8474faf
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
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
import argparse
import codecs
import re
import tempfile
from pathlib import Path

import numpy as np
import soundfile as sf
import tomli
import torch
import torchaudio
import tqdm
from cached_path import cached_path
from einops import rearrange
from pydub import AudioSegment, silence
from transformers import pipeline
from vocos import Vocos

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

parser = argparse.ArgumentParser(
    prog="python3 inference-cli.py",
    description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.",
    epilog="Specify  options above  to override  one or more settings from config.",
)
parser.add_argument(
    "-c",
    "--config",
    help="Configuration file. Default=cli-config.toml",
    default="inference-cli.toml",
)
parser.add_argument(
    "-m",
    "--model",
    help="F5-TTS | E2-TTS",
)
parser.add_argument(
    "-p",
    "--ckpt_file",
    help="The Checkpoint .pt",
)
parser.add_argument(
    "-v",
    "--vocab_file",
    help="The vocab .txt",
)
parser.add_argument(
    "-r",
    "--ref_audio",
    type=str,
    help="Reference audio file < 15 seconds."
)
parser.add_argument(
    "-s",
    "--ref_text",
    type=str,
    default="666",
    help="Subtitle for the reference audio."
)
parser.add_argument(
    "-t",
    "--gen_text",
    type=str,
    help="Text to generate.",
)
parser.add_argument(
    "-f",
    "--gen_file",
    type=str,
    help="File with text to generate. Ignores --text",
)
parser.add_argument(
    "-o",
    "--output_dir",
    type=str,
    help="Path to output folder..",
)
parser.add_argument(
    "--remove_silence",
    help="Remove silence.",
)
parser.add_argument(
    "--load_vocoder_from_local",
    action="store_true",
    help="load vocoder from local. Default: ../checkpoints/charactr/vocos-mel-24khz",
)
args = parser.parse_args()

config = tomli.load(open(args.config, "rb"))

ref_audio = args.ref_audio if args.ref_audio else config["ref_audio"]
ref_text = args.ref_text if args.ref_text != "666" else config["ref_text"]
gen_text = args.gen_text if args.gen_text else config["gen_text"]
gen_file = args.gen_file if args.gen_file else config["gen_file"]
if gen_file:
    gen_text = codecs.open(gen_file, "r", "utf-8").read()
output_dir = args.output_dir if args.output_dir else config["output_dir"]
model = args.model if args.model else config["model"]
ckpt_file = args.ckpt_file if args.ckpt_file else ""
vocab_file = args.vocab_file if args.vocab_file else ""
remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"]
wave_path = Path(output_dir)/"out.wav"
spectrogram_path = Path(output_dir)/"out.png"
vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"

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

if args.load_vocoder_from_local:
    print(f"Load vocos from local path {vocos_local_path}")
    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:
    print("Download Vocos from huggingface charactr/vocos-mel-24khz")
    vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")

print(f"Using {device} device")

# --------------------- Settings -------------------- #

target_sample_rate = 24000
n_mel_channels = 100
hop_length = 256
target_rms = 0.1
nfe_step = 32  # 16, 32
cfg_strength = 2.0
ode_method = "euler"
sway_sampling_coef = -1.0
speed = 1.0
# fix_duration = 27  # None or float (duration in seconds)
fix_duration = None

def load_model(model_cls, model_cfg, ckpt_path,file_vocab):
    
    if file_vocab=="":
        file_vocab="Emilia_ZH_EN"
        tokenizer="pinyin"
    else:
        tokenizer="custom"

    print("\nvocab : ", vocab_file,tokenizer) 
    print("tokenizer : ", tokenizer) 
    print("model : ", ckpt_path,"\n")    

    vocab_char_map, vocab_size = get_tokenizer(file_vocab, tokenizer)
    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)

    model = load_checkpoint(model, ckpt_path, device, use_ema = True)

    return model

# load models
F5TTS_model_cfg = dict(
    dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4
)
E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)

if model == "F5-TTS":

    if ckpt_file == "": 
       repo_name= "F5-TTS"
       exp_name = "F5TTS_Base"
       ckpt_step= 1200000
       ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))

    ema_model = load_model(DiT, F5TTS_model_cfg, ckpt_file,vocab_file)

elif model == "E2-TTS":
    if ckpt_file == "": 
       repo_name= "E2-TTS"
       exp_name = "E2TTS_Base"
       ckpt_step= 1200000
       ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
    
    ema_model = load_model(UNetT, E2TTS_model_cfg, ckpt_file,vocab_file)

asr_pipe = pipeline(
    "automatic-speech-recognition",
    model="openai/whisper-large-v3-turbo",
    torch_dtype=torch.float16,
    device=device,
)

def chunk_text(text, max_chars=135):
    """
    Splits the input text into chunks, each with a maximum number of characters.
    Args:
        text (str): The text to be split.
        max_chars (int): The maximum number of characters per chunk.
    Returns:
        List[str]: A list of text chunks.
    """
    chunks = []
    current_chunk = ""
    # Split the text into sentences based on punctuation followed by whitespace
    sentences = re.split(r'(?<=[;:,.!?])\s+|(?<=[;:,。!?])', text)

    for sentence in sentences:
        if len(current_chunk.encode('utf-8')) + len(sentence.encode('utf-8')) <= max_chars:
            current_chunk += sentence + " " if sentence and len(sentence[-1].encode('utf-8')) == 1 else sentence
        else:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = sentence + " " if sentence and len(sentence[-1].encode('utf-8')) == 1 else sentence

    if current_chunk:
        chunks.append(current_chunk.strip())

    return chunks

    #ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors
    #if not Path(ckpt_path).exists():
        #ckpt_path = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))

def infer_batch(ref_audio, ref_text, gen_text_batches, model, remove_silence, cross_fade_duration=0.15):
    audio, sr = ref_audio
    if audio.shape[0] > 1:
        audio = torch.mean(audio, dim=0, keepdim=True)

    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)

    generated_waves = []
    spectrograms = []

    if len(ref_text[-1].encode('utf-8')) == 1:
        ref_text = ref_text + " "
    for i, gen_text in enumerate(tqdm.tqdm(gen_text_batches)):
        # Prepare the text
        text_list = [ref_text + gen_text]
        final_text_list = convert_char_to_pinyin(text_list)

        # Calculate duration
        ref_audio_len = audio.shape[-1] // hop_length
        ref_text_len = len(ref_text.encode('utf-8'))
        gen_text_len = len(gen_text.encode('utf-8'))
        duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)

        # inference
        with torch.inference_mode():
            generated, _ = ema_model.sample(
                cond=audio,
                text=final_text_list,
                duration=duration,
                steps=nfe_step,
                cfg_strength=cfg_strength,
                sway_sampling_coef=sway_sampling_coef,
            )

        generated = generated.to(torch.float32)
        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

        # wav -> numpy
        generated_wave = generated_wave.squeeze().cpu().numpy()
        
        generated_waves.append(generated_wave)
        spectrograms.append(generated_mel_spec[0].cpu().numpy())

    # Combine all generated waves with cross-fading
    if cross_fade_duration <= 0:
        # Simply concatenate
        final_wave = np.concatenate(generated_waves)
    else:
        final_wave = generated_waves[0]
        for i in range(1, len(generated_waves)):
            prev_wave = final_wave
            next_wave = generated_waves[i]

            # Calculate cross-fade samples, ensuring it does not exceed wave lengths
            cross_fade_samples = int(cross_fade_duration * target_sample_rate)
            cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave))

            if cross_fade_samples <= 0:
                # No overlap possible, concatenate
                final_wave = np.concatenate([prev_wave, next_wave])
                continue

            # Overlapping parts
            prev_overlap = prev_wave[-cross_fade_samples:]
            next_overlap = next_wave[:cross_fade_samples]

            # Fade out and fade in
            fade_out = np.linspace(1, 0, cross_fade_samples)
            fade_in = np.linspace(0, 1, cross_fade_samples)

            # Cross-faded overlap
            cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in

            # Combine
            new_wave = np.concatenate([
                prev_wave[:-cross_fade_samples],
                cross_faded_overlap,
                next_wave[cross_fade_samples:]
            ])

            final_wave = new_wave

    # Create a combined spectrogram
    combined_spectrogram = np.concatenate(spectrograms, axis=1)

    return final_wave, combined_spectrogram

def process_voice(ref_audio_orig, ref_text):
    print("Converting", ref_audio_orig)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
        aseg = AudioSegment.from_file(ref_audio_orig)

        non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000)
        non_silent_wave = AudioSegment.silent(duration=0)
        for non_silent_seg in non_silent_segs:
            non_silent_wave += non_silent_seg
        aseg = non_silent_wave

        audio_duration = len(aseg)
        if audio_duration > 15000:
            print("Audio is over 15s, clipping to only first 15s.")
            aseg = aseg[:15000]
        aseg.export(f.name, format="wav")
        ref_audio = f.name

    if not ref_text.strip():
        print("No reference text provided, transcribing reference audio...")
        ref_text = asr_pipe(
            ref_audio,
            chunk_length_s=30,
            batch_size=128,
            generate_kwargs={"task": "transcribe"},
            return_timestamps=False,
        )["text"].strip()
        print("Finished transcription")
    else:
        print("Using custom reference text...")
    return ref_audio, ref_text    

def infer(ref_audio, ref_text, gen_text, model, remove_silence, cross_fade_duration=0.15):
    # Add the functionality to ensure it ends with ". "
    if not ref_text.endswith(". ") and not ref_text.endswith("。"):
        if ref_text.endswith("."):
            ref_text += " "
        else:
            ref_text += ". "

    # Split the input text into batches
    audio, sr = torchaudio.load(ref_audio)
    max_chars = int(len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr))
    gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
    for i, gen_text in enumerate(gen_text_batches):
        print(f'gen_text {i}', gen_text)
    
    print(f"Generating audio using {model} in {len(gen_text_batches)} batches, loading models...")
    return infer_batch((audio, sr), ref_text, gen_text_batches, model, remove_silence, cross_fade_duration)
    

def process(ref_audio, ref_text, text_gen, model, remove_silence):
    main_voice = {"ref_audio":ref_audio, "ref_text":ref_text}
    if "voices" not in config:
        voices = {"main": main_voice}
    else:
        voices = config["voices"]
        voices["main"] = main_voice
    for voice in voices:
        voices[voice]['ref_audio'], voices[voice]['ref_text'] = process_voice(voices[voice]['ref_audio'], voices[voice]['ref_text'])
        print("Voice:", voice)
        print("Ref_audio:", voices[voice]['ref_audio'])
        print("Ref_text:", voices[voice]['ref_text'])

    generated_audio_segments = []
    reg1 = r'(?=\[\w+\])'
    chunks = re.split(reg1, text_gen)
    reg2 = r'\[(\w+)\]'
    for text in chunks:
        match = re.match(reg2, text)
        if not match or voice not in voices:
            voice = "main"
        else:
            voice = match[1]
        text = re.sub(reg2, "", text)
        gen_text = text.strip()
        ref_audio = voices[voice]['ref_audio']
        ref_text = voices[voice]['ref_text']
        print(f"Voice: {voice}")
        audio, spectragram = infer(ref_audio, ref_text, gen_text, model,remove_silence)
        generated_audio_segments.append(audio)

    if generated_audio_segments:
        final_wave = np.concatenate(generated_audio_segments)
        with open(wave_path, "wb") as f:
            sf.write(f.name, final_wave, target_sample_rate)
            # Remove silence
            if remove_silence:
                aseg = AudioSegment.from_file(f.name)
                non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)
                non_silent_wave = AudioSegment.silent(duration=0)
                for non_silent_seg in non_silent_segs:
                    non_silent_wave += non_silent_seg
                aseg = non_silent_wave
                aseg.export(f.name, format="wav")
            print(f.name)


process(ref_audio, ref_text, gen_text, model, remove_silence)