File size: 10,250 Bytes
e495c6a | 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 | import fastapi
import shutil
import os
import zipfile
import io
import uvicorn
import threading
import glob
from typing import List
import torch
import gdown
from soundfile import write
from torchaudio import load
from librosa import resample
import logging
logging.basicConfig(level=logging.DEBUG)
from sgmse import ScoreModel
from sgmse.util.other import pad_spec
class ModelAPI:
def __init__(self, host, port):
self.host = host
self.port = port
self.base_path = os.path.join(os.path.expanduser("~"), ".modelapi")
self.noisy_audio_path = os.path.join(self.base_path, "noisy_audio")
self.enhanced_audio_path = os.path.join(self.base_path, "enhanced_audio")
app_dir = os.path.dirname(os.path.abspath(__file__))
self.ckpt_path = glob.glob(os.path.join(app_dir, "*.ckpt"))[0]
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.corrector = "ald"
self.corrector_steps = 1
self.snr = 0.5
self.N = 30
for audio_path in [self.noisy_audio_path, self.enhanced_audio_path]:
if not os.path.exists(audio_path):
os.makedirs(audio_path)
for filename in os.listdir(audio_path):
file_path = os.path.join(audio_path, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
raise e
self.app = fastapi.FastAPI()
self._setup_routes()
def _prepare(self):
"""Miners should modify this function to fit their fine-tuned models.
This function will make any preparations necessary to initialize the
speech enhancement model (i.e. downloading checkpoint files, etc.)
"""
self.model = ScoreModel.load_from_checkpoint(self.ckpt_path, self.device)
self.model.t_eps = 0.03
self.model.eval()
def _enhance(self):
"""
Miners should modify this function to fit their fine-tuned models.
This function will:
1. Open each noisy .wav file
2. Enhance the audio with the model
3. Save the enhanced audio in .wav format to ModelAPI.enhanced_audio_path
"""
if self.model.backbone == "ncsnpp_48k":
target_sr = 48000
pad_mode = "reflection"
elif self.model.backbone == "ncsnpp_v2":
target_sr = 16000
pad_mode = "reflection"
print("using ncsnpp_v2")
else:
target_sr = 16000
pad_mode = "zero_pad"
noisy_files = sorted(glob.glob(os.path.join(self.noisy_audio_path, "*.wav")))
for noisy_file in noisy_files:
filename = noisy_file.replace(self.noisy_audio_path, "")
filename = filename[1:] if filename.startswith("/") else filename
y, sr = load(noisy_file)
if sr != target_sr:
y = torch.tensor(resample(y.numpy(), orig_sr=sr, target_sr=target_sr))
T_orig = y.size(1)
# Normalize
norm_factor = y.abs().max()
y = y / norm_factor
# Prepare DNN input
Y = torch.unsqueeze(
self.model._forward_transform(self.model._stft(y.to(self.device))), 0
)
Y = pad_spec(Y, mode=pad_mode)
# Reverse sampling
if self.model.sde.__class__.__name__ == "OUVESDE":
if self.model.sde.sampler_type == "pc":
sampler = self.model.get_pc_sampler(
"reverse_diffusion",
self.corrector,
Y.to(self.device),
N=self.N,
corrector_steps=self.corrector_steps,
snr=self.snr,
)
elif self.model.sde.sampler_type == "ode":
sampler = self.model.get_ode_sampler(Y.to(self.device), N=self.N)
else:
raise ValueError(f"Sampler type {args.sampler_type} not supported")
elif self.model.sde.__class__.__name__ == "SBVESDE":
sampler_type = (
"ode"
if self.model.sde.sampler_type == "pc"
else self.model.sde.sampler_type
)
sampler = self.model.get_sb_sampler(
sde=self.model.sde, y=Y.cuda(), sampler_type=sampler_type
)
else:
raise ValueError(
f"SDE {self.model.sde.__class__.__name__} not supported"
)
sample, _ = sampler()
x_hat = self.model.to_audio(sample.squeeze(), T_orig)
x_hat = x_hat * norm_factor
os.makedirs(
os.path.dirname(os.path.join(self.enhanced_audio_path, filename)),
exist_ok=True,
)
write(
os.path.join(self.enhanced_audio_path, filename),
x_hat.cpu().numpy(),
target_sr,
)
def _setup_routes(self):
self.app.get("/status/")(self.get_status)
self.app.post("/prepare/")(self.prepare)
self.app.post("/upload-audio/")(self.upload_audio)
self.app.post("/enhance/")(self.enhance_audio)
self.app.get("/download-enhanced/")(self.download_enhanced)
self.app.post("/reset/")(self.reset)
def get_status(self):
try:
return {"container_running": True}
except Exception as e:
logging.error(f"Error getting status: {e}")
raise fastapi.HTTPException(
status_code=500, detail="An error occurred while fetching API status."
)
def prepare(self):
try:
self._prepare()
return {"preparations": True}
except Exception as e:
logging.error(f"Error during preparations: {e}")
return fastapi.HTTPException(
status_code=500, detail="An error occurred while fetching API status."
)
def upload_audio(self, files: List[fastapi.UploadFile] = fastapi.File(...)):
uploaded_files = []
for file in files:
try:
file_path = os.path.join(self.noisy_audio_path, file.filename)
with open(file_path, "wb") as f:
while contents := file.file.read(1024 * 1024):
f.write(contents)
uploaded_files.append(file.filename)
except Exception as e:
logging.error(f"Error uploading files: {e}")
raise fastapi.HTTPException(
status_code=500,
detail="An error occurred while uploading the noisy files.",
)
finally:
file.file.close()
print(f"uploaded files: {uploaded_files}")
return {"uploaded_files": uploaded_files, "status": True}
def enhance_audio(self):
try:
# Enhance audio
self._enhance()
# Obtain list of file paths for enhanced audio
wav_files = glob.glob(os.path.join(self.enhanced_audio_path, "*.wav"))
# Extract just the file names
enhanced_files = [os.path.basename(file) for file in wav_files]
return {"status": True}
except Exception as e:
print(f"Exception occured during enhancement: {e}")
raise fastapi.HTTPException(
status_code=500,
detail="An error occurred while enhancing the noisy files.",
)
def download_enhanced(self):
try:
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, "w") as zip_file:
for wav_file in glob.glob(
os.path.join(self.enhanced_audio_path, "*.wav")
):
zip_file.write(wav_file, arcname=os.path.basename(wav_file))
zip_buffer.seek(0)
return fastapi.responses.StreamingResponse(
iter([zip_buffer.getvalue()]), # Stream the in-memory content
media_type="application/zip",
headers={
"Content-Disposition": "attachment; filename=enhanced_audio_files.zip"
},
)
except Exception as e:
logging.error(f"Error during enhanced files download: {e}")
raise fastapi.HTTPException(
status_code=500,
detail=f"An error occurred while creating the download file: {str(e)}",
)
def reset(self):
"""
Removes all audio files in preparation for another batch of enhancement.
"""
for directory in [self.noisy_audio_path, self.enhanced_audio_path]:
if not os.path.isdir(directory):
continue
for filename in os.listdir(directory):
filepath = os.path.join(directory, filename)
if os.path.isfile(filepath):
try:
os.remove(filepath)
except Exception as e:
print(f"Error removing {filepath}: {e}")
return {
"status": False,
"noisy": os.listdir(self.noisy_audio_path),
"enhanced": os.listdir(self.enhanced_audio_path),
}
return {
"status": True,
"noisy": os.listdir(self.noisy_audio_path),
"enhanced": os.listdir(self.enhanced_audio_path),
}
def run(self):
uvicorn.run(self.app, host=self.host, port=self.port)
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