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import sys
import os
#replace the path with your hifigan path to import Generator from models.py
sys.path.append("hifigan")
import argparse
import torch
from espnet2.bin.tts_inference import Text2Speech
from models import Generator
from scipy.io.wavfile import write
from meldataset import MAX_WAV_VALUE
from env import AttrDict
import json
import yaml
import concurrent.futures
import numpy as np
import time
import re
from text_preprocess_for_inference import TTSDurAlignPreprocessor, CharTextPreprocessor, TTSPreprocessor
SAMPLING_RATE = 22050
def load_hifigan_vocoder(language, gender, device):
# Load HiFi-GAN vocoder configuration file and generator model for the specified language and gender
vocoder_config = f"vocoder/{gender}/{language}/config.json"
vocoder_generator = f"vocoder/{gender}/{language}/generator"
# Read the contents of the vocoder configuration file
with open(vocoder_config, 'r') as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
torch.manual_seed(h.seed)
# Move the generator model to the specified device (CPU or GPU)
device = torch.device(device)
generator = Generator(h).to(device)
state_dict_g = torch.load(vocoder_generator, device)
generator.load_state_dict(state_dict_g['generator'])
generator.eval()
generator.remove_weight_norm()
# Return the loaded and prepared HiFi-GAN generator model
return generator
def load_fastspeech2_model(language, gender, device):
#updating the config.yaml fiel based on language and gender
with open(f"{language}/{gender}/model/config.yaml", "r") as file:
config = yaml.safe_load(file)
current_working_directory = os.getcwd()
feat="model/feats_stats.npz"
pitch="model/pitch_stats.npz"
energy="model/energy_stats.npz"
feat_path=os.path.join(current_working_directory,language,gender,feat)
pitch_path=os.path.join(current_working_directory,language,gender,pitch)
energy_path=os.path.join(current_working_directory,language,gender,energy)
config["normalize_conf"]["stats_file"] = feat_path
config["pitch_normalize_conf"]["stats_file"] = pitch_path
config["energy_normalize_conf"]["stats_file"] = energy_path
with open(f"{language}/{gender}/model/config.yaml", "w") as file:
yaml.dump(config, file)
tts_model = f"{language}/{gender}/model/model.pth"
tts_config = f"{language}/{gender}/model/config.yaml"
return Text2Speech(train_config=tts_config, model_file=tts_model, device=device)
def text_synthesis(language, gender, sample_text, vocoder, model, MAX_WAV_VALUE, device, alpha):
# Perform Text-to-Speech synthesis
with torch.no_grad():
# Load the FastSpeech2 model for the specified language and gender
# model = load_fastspeech2_model(language, gender, device)
# Generate mel-spectrograms from the input text using the FastSpeech2 model
out = model(sample_text, decode_conf={"alpha": alpha})
print("TTS Done")
x = out["feat_gen_denorm"].T.unsqueeze(0) * 2.3262
x = x.to(device)
# Use the HiFi-GAN vocoder to convert mel-spectrograms to raw audio waveforms
y_g_hat = vocoder(x)
audio = y_g_hat.squeeze()
audio = audio * MAX_WAV_VALUE
audio = audio.cpu().numpy().astype('int16')
# Return the synthesized audio
return audio
def split_into_chunks(text, words_per_chunk=100):
words = text.split()
chunks = [words[i:i + words_per_chunk] for i in range(0, len(words), words_per_chunk)]
return [' '.join(chunk) for chunk in chunks]
def extract_text_alpha_chunks(text, default_alpha=1.0):
alpha_pattern = r"<alpha=([0-9.]+)>"
sil_pattern = r"<sil=([0-9.]+)(ms|s)>"
chunks = []
alpha = default_alpha
alpha_blocks = re.split(alpha_pattern, text)
i = 0
while i < len(alpha_blocks):
if i == 0:
current_block = alpha_blocks[i]
i += 1
else:
alpha = float(alpha_blocks[i])
i += 1
current_block = alpha_blocks[i] if i < len(alpha_blocks) else ""
i += 1
sil_matches = list(re.finditer(sil_pattern, current_block))
sil_placeholders = {}
for j, match in enumerate(sil_matches):
tag = match.group(0)
value = float(match.group(1))
unit = match.group(2)
duration = value / 1000.0 if unit == "ms" else value
placeholder = f"__SIL_{j}__"
sil_placeholders[placeholder] = duration
current_block = current_block.replace(tag, f" {placeholder} ")
sentences = [s.strip() for s in current_block.split('.') if s.strip()]
for sentence in sentences:
words = sentence.split()
buffer = []
for word in words:
if word in sil_placeholders:
if buffer:
chunks.append((" ".join(buffer), alpha, False, None))
buffer = []
chunks.append(("", alpha, True, sil_placeholders[word]))
else:
buffer.append(word)
if buffer:
chunks.append((" ".join(buffer), alpha, False, None))
return chunks
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Text-to-Speech Inference")
parser.add_argument("--language", type=str, required=True, help="Language (e.g., hindi)")
parser.add_argument("--gender", type=str, required=True, help="Gender (e.g., female)")
parser.add_argument("--sample_text", type=str, required=True, help="Text to be synthesized")
parser.add_argument("--output_file", type=str, default="", help="Output WAV file path")
parser.add_argument("--alpha", type=float, default=1, help="Alpha Parameter for speed control (e.g. 1.1 (slow) or 0.8 (fast))")
args = parser.parse_args()
phone_dictionary = {}
# Set the device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load the HiFi-GAN vocoder with dynamic language and gender
vocoder = load_hifigan_vocoder(args.language, args.gender, device)
model = load_fastspeech2_model(args.language, args.gender, device)
if args.language == "urdu" or args.language == "punjabi":
preprocessor = CharTextPreprocessor()
elif args.language == "english":
preprocessor = TTSPreprocessor()
else:
preprocessor = TTSDurAlignPreprocessor()
start_time = time.time()
audio_arr = []
result = split_into_chunks(args.sample_text)
text_alpha_chunks = extract_text_alpha_chunks(args.sample_text, args.alpha)
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = []
for chunk_text, alpha_val, is_silence, sil_duration in text_alpha_chunks:
if is_silence:
silence_samples = int(sil_duration * SAMPLING_RATE)
silence_audio = np.zeros(silence_samples, dtype=np.int16)
futures.append(silence_audio)
else:
preprocessed_text, _ = preprocessor.preprocess(chunk_text, args.language, args.gender, phone_dictionary)
preprocessed_text = " ".join(preprocessed_text)
future = executor.submit(
text_synthesis, args.language, args.gender, preprocessed_text,
vocoder, model, MAX_WAV_VALUE, device, alpha_val
)
futures.append(future)
for item in futures:
if isinstance(item, np.ndarray):
audio_arr.append(item)
else:
audio_arr.append(item.result())
result_array = np.concatenate(audio_arr, axis=0)
output_file = args.output_file if args.output_file else f"{args.language}_{args.gender}_output.wav"
write(output_file, SAMPLING_RATE, result_array)
print(f"Synthesis completed in {time.time()-start_time:.2f} sec → {output_file}") |