-
Notifications
You must be signed in to change notification settings - Fork 573
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #60 from lewangdev/main
add an openai compatible api, thanks lewangdev.
- Loading branch information
Showing
3 changed files
with
188 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,5 @@ | ||
outputs/ | ||
WangZeJun/ | ||
*.pyc | ||
.vscode/ | ||
__pycache__/ |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,179 @@ | ||
import logging | ||
import os | ||
import io | ||
import torch | ||
import glob | ||
|
||
from fastapi import FastAPI, Response | ||
from pydantic import BaseModel | ||
|
||
from frontend import g2p_cn_en | ||
from models.prompt_tts_modified.jets import JETSGenerator | ||
from models.prompt_tts_modified.simbert import StyleEncoder | ||
from transformers import AutoTokenizer | ||
import numpy as np | ||
import soundfile as sf | ||
from pydub import AudioSegment | ||
from yacs import config as CONFIG | ||
from config.joint.config import Config | ||
|
||
LOGGER = logging.getLogger(__name__) | ||
|
||
DEFAULTS = { | ||
} | ||
|
||
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") | ||
print(DEVICE) | ||
config = Config() | ||
MAX_WAV_VALUE = 32768.0 | ||
|
||
|
||
def get_env(key): | ||
return os.environ.get(key, DEFAULTS.get(key)) | ||
|
||
|
||
def get_int_env(key): | ||
return int(get_env(key)) | ||
|
||
|
||
def get_float_env(key): | ||
return float(get_env(key)) | ||
|
||
|
||
def get_bool_env(key): | ||
return get_env(key).lower() == 'true' | ||
|
||
|
||
def scan_checkpoint(cp_dir, prefix, c=8): | ||
pattern = os.path.join(cp_dir, prefix + '?'*c) | ||
cp_list = glob.glob(pattern) | ||
if len(cp_list) == 0: | ||
return None | ||
return sorted(cp_list)[-1] | ||
|
||
|
||
def get_models(): | ||
|
||
am_checkpoint_path = scan_checkpoint( | ||
f'{config.output_directory}/prompt_tts_open_source_joint/ckpt', 'g_') | ||
|
||
# f'{config.output_directory}/style_encoder/ckpt/checkpoint_163431' | ||
style_encoder_checkpoint_path = scan_checkpoint( | ||
f'{config.output_directory}/style_encoder/ckpt', 'checkpoint_', 6) | ||
|
||
with open(config.model_config_path, 'r') as fin: | ||
conf = CONFIG.load_cfg(fin) | ||
|
||
conf.n_vocab = config.n_symbols | ||
conf.n_speaker = config.speaker_n_labels | ||
|
||
style_encoder = StyleEncoder(config) | ||
model_CKPT = torch.load(style_encoder_checkpoint_path, map_location="cpu") | ||
model_ckpt = {} | ||
for key, value in model_CKPT['model'].items(): | ||
new_key = key[7:] | ||
model_ckpt[new_key] = value | ||
style_encoder.load_state_dict(model_ckpt) | ||
generator = JETSGenerator(conf).to(DEVICE) | ||
|
||
model_CKPT = torch.load(am_checkpoint_path, map_location=DEVICE) | ||
generator.load_state_dict(model_CKPT['generator']) | ||
generator.eval() | ||
|
||
tokenizer = AutoTokenizer.from_pretrained(config.bert_path) | ||
|
||
with open(config.token_list_path, 'r') as f: | ||
token2id = {t.strip(): idx for idx, t, in enumerate(f.readlines())} | ||
|
||
with open(config.speaker2id_path, encoding='utf-8') as f: | ||
speaker2id = {t.strip(): idx for idx, t in enumerate(f.readlines())} | ||
|
||
return (style_encoder, generator, tokenizer, token2id, speaker2id) | ||
|
||
|
||
def get_style_embedding(prompt, tokenizer, style_encoder): | ||
prompt = tokenizer([prompt], return_tensors="pt") | ||
input_ids = prompt["input_ids"] | ||
token_type_ids = prompt["token_type_ids"] | ||
attention_mask = prompt["attention_mask"] | ||
with torch.no_grad(): | ||
output = style_encoder( | ||
input_ids=input_ids, | ||
token_type_ids=token_type_ids, | ||
attention_mask=attention_mask, | ||
) | ||
style_embedding = output["pooled_output"].cpu().squeeze().numpy() | ||
return style_embedding | ||
|
||
|
||
def emotivoice_tts(text, prompt, content, speaker, models): | ||
(style_encoder, generator, tokenizer, token2id, speaker2id) = models | ||
|
||
style_embedding = get_style_embedding(prompt, tokenizer, style_encoder) | ||
content_embedding = get_style_embedding(content, tokenizer, style_encoder) | ||
|
||
speaker = speaker2id[speaker] | ||
|
||
text_int = [token2id[ph] for ph in text.split()] | ||
|
||
sequence = torch.from_numpy(np.array(text_int)).to( | ||
DEVICE).long().unsqueeze(0) | ||
sequence_len = torch.from_numpy(np.array([len(text_int)])).to(DEVICE) | ||
style_embedding = torch.from_numpy(style_embedding).to(DEVICE).unsqueeze(0) | ||
content_embedding = torch.from_numpy( | ||
content_embedding).to(DEVICE).unsqueeze(0) | ||
speaker = torch.from_numpy(np.array([speaker])).to(DEVICE) | ||
|
||
with torch.no_grad(): | ||
|
||
infer_output = generator( | ||
inputs_ling=sequence, | ||
inputs_style_embedding=style_embedding, | ||
input_lengths=sequence_len, | ||
inputs_content_embedding=content_embedding, | ||
inputs_speaker=speaker, | ||
alpha=1.0 | ||
) | ||
|
||
audio = infer_output["wav_predictions"].squeeze() * MAX_WAV_VALUE | ||
audio = audio.cpu().numpy().astype('int16') | ||
|
||
return audio | ||
|
||
|
||
speakers = config.speakers | ||
models = get_models() | ||
app = FastAPI() | ||
|
||
from typing import Optional | ||
class SpeechRequest(BaseModel): | ||
input: str | ||
voice: str = '8051' | ||
prompt: Optional[str] = '' | ||
language: Optional[str] = 'zh_us' | ||
model: Optional[str] = 'emoti-voice' | ||
response_format: Optional[str] = 'mp3' | ||
speed: Optional[float] = 1.0 | ||
|
||
|
||
@app.post("/v1/audio/speech") | ||
def text_to_speech(speechRequest: SpeechRequest): | ||
|
||
text = g2p_cn_en(speechRequest.input) | ||
np_audio = emotivoice_tts(text, speechRequest.prompt, | ||
speechRequest.input, speechRequest.voice, | ||
models) | ||
wav_buffer = io.BytesIO() | ||
sf.write(file=wav_buffer, data=np_audio, | ||
samplerate=config.sampling_rate, format='WAV') | ||
buffer = wav_buffer | ||
response_format = speechRequest.response_format | ||
if response_format != 'wav': | ||
wav_audio = AudioSegment( | ||
wav_buffer.getvalue(), frame_rate=config.sampling_rate, | ||
sample_width=2, channels=1) | ||
buffer = io.BytesIO() | ||
wav_audio.export(buffer, format=response_format) | ||
|
||
return Response(content=buffer.getvalue(), | ||
media_type=f"audio/{response_format}") |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,4 @@ | ||
fastapi | ||
python-multipart | ||
uvicorn[standard] | ||
pydub |