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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import gc
import io
import logging
import threading
from dataclasses import dataclass
from typing import Dict, List
import torch
import torchaudio
import torchaudio.functional as audio_F
from .align_utils import get_spans, load_model_dict, merge_repeats, time_to_frame
from .audio_reading_tools import wav_to_bytes
# Global logger for this module
logger = logging.getLogger(__name__)
@dataclass(kw_only=True)
class AudioAlignmentConfig:
model_path_name: str = ""
emission_interval: int = 30
audio_format: str = "flac"
use_star: bool = False
device: str = "cuda"
class AudioAlignment:
"""Thread-safe singleton for audio-text alignment."""
_instance = None
_lock = threading.Lock()
scale: int = 1000
def __new__(cls):
if cls._instance is None:
with cls._lock:
# Double-check locking pattern
if cls._instance is None:
cls._instance = super(AudioAlignment, cls).__new__(cls)
cls._instance._initialize()
return cls._instance
def _initialize(self):
"""Initialize the singleton instance (called only once)."""
logger.info("Initializing AudioAlignment model...")
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
config = AudioAlignmentConfig(
device=str(device),
use_star=False, # Set to False for standard alignment
)
self.config = config
# FIXME: pass model name correctly
logger.info("Loading forced alignment model and dictionary...")
self.model, self.dictionary = load_model_dict()
self.device = torch.device(config.device)
self.model.to(self.device)
if self.config.use_star:
self.dictionary["<star>"] = len(self.dictionary)
self.blank = self.dictionary["<blank>"]
self.inverse_dictionary = {v: k for k, v in self.dictionary.items()}
logger.info(
f"AudioAlignment model loaded successfully on device: {self.device}"
)
@torch.inference_mode()
def generate_emissions(self, waveform: torch.Tensor, reading_sr):
emission_interval = self.config.emission_interval
total_duration = waveform.size(1) / reading_sr
emissions_arr = []
i = 0
while i < total_duration:
segment_start_time, segment_end_time = (i, i + emission_interval)
context = emission_interval * 0.1
input_start_time = max(segment_start_time - context, 0)
input_end_time = min(segment_end_time + context, total_duration)
waveform_split = waveform[
:,
int(reading_sr * input_start_time) : int(reading_sr * (input_end_time)),
]
model_outs, _ = self.model(waveform_split)
emissions_ = model_outs[0]
emission_start_frame = time_to_frame(segment_start_time)
emission_end_frame = time_to_frame(segment_end_time)
offset = time_to_frame(input_start_time)
emissions_ = emissions_[
emission_start_frame - offset : emission_end_frame - offset, :
]
emissions_arr.append(emissions_)
i += emission_interval
emissions = torch.cat(emissions_arr, dim=0).squeeze()
emissions = torch.log_softmax(emissions, dim=-1)
stride = float(waveform.size(1) * self.scale / emissions.size(0) / reading_sr)
return emissions, stride
@torch.inference_mode()
def get_one_row_alignments(
self, audio_arr, reading_sr, tokens: List[str]
) -> List[Dict]:
"""Internal method to perform forced alignment."""
buffer = audio_arr.tobytes()
waveform, audio_sf = torchaudio.load(io.BytesIO(buffer))
waveform = waveform.to(self.device)
assert audio_sf == reading_sr
emissions, stride = self.generate_emissions(waveform, reading_sr)
waveform = waveform.cpu()
if self.config.use_star:
T, _ = emissions.size()
emissions = torch.cat(
[emissions, torch.zeros(T, 1, device=self.device)], dim=1
)
if self.config.use_star:
tokens = ["<star>"] + tokens
token_indices = [
self.dictionary[c]
for c in " ".join(tokens).split(" ")
if c in self.dictionary
]
targets = torch.tensor(token_indices, dtype=torch.int32, device=self.device)
input_lengths = torch.tensor(emissions.shape[0]).unsqueeze(-1)
target_lengths = torch.tensor(targets.shape[0]).unsqueeze(-1)
path, _ = audio_F.forced_align(
emissions.unsqueeze(0),
targets.unsqueeze(0),
input_lengths,
target_lengths,
blank=self.blank,
)
path = path.squeeze().to("cpu").tolist()
segments = merge_repeats(path, self.inverse_dictionary)
spans = get_spans(tokens, segments)
audio_segments = []
for span in spans:
seg_start_idx, seg_end_idx = span[0].start, span[-1].end
segment_start_sec = seg_start_idx * stride / self.scale
segment_end_sec = seg_end_idx * stride / self.scale
start_frame = int(segment_start_sec * reading_sr)
end_frame = int(segment_end_sec * reading_sr)
trimmed_waveform = waveform[:, start_frame:end_frame]
audio_segments.append(
{
"segment_start_sec": segment_start_sec,
"segment_end_sec": segment_end_sec,
"segment_duration": segment_end_sec - segment_start_sec,
"segment_audio_bytes": wav_to_bytes(
trimmed_waveform, reading_sr, self.config.audio_format
),
}
)
return audio_segments
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