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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
from dataclasses import dataclass
from typing import Dict, List
import pyarrow as pa
import torch
import torchaudio
import torchaudio.functional as audio_F
from stopes.modules.partitioned_data_mapper import BatchMapper
from align_utils import (
get_spans,
load_model_dict,
merge_repeats,
time_to_frame,
)
from audio_reading_tools import wav_to_bytes
@dataclass(kw_only=True)
class AlignmentStruct:
segement_tokens: str
audio: str
segment_audio_bytes: str = "segment_audio_bytes"
segment_duration: str = "segment_duration"
segment_start_sec: str = "segment_start_sec"
@dataclass(kw_only=True)
class AudioAlignmentConfig:
alignment_column: AlignmentStruct
model_path_name: str = ""
emission_interval: int = 30
sample_rate: int = 16000
audio_format: str = "flac"
use_star: bool = False
device: str = "cuda"
class AudioAlignment(BatchMapper):
scale: int = 1000
def __init__(self, config: AudioAlignmentConfig):
super().__init__(config)
# FIXME: pass model name correctly
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()}
self._alignment_column = self.config.alignment_column
@torch.inference_mode()
def generate_emissions(self, waveform: torch.Tensor):
reading_sr = self.config.sample_rate
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
def get_one_row_alignments(
self,
audio_arr,
tokens: List[str],
):
reading_sr = self.config.sample_rate
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)
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(
{
self._alignment_column.segment_start_sec: segment_start_sec,
self._alignment_column.segment_duration: segment_end_sec
- segment_start_sec,
self._alignment_column.segment_audio_bytes: wav_to_bytes(
trimmed_waveform, reading_sr, self.config.audio_format
),
}
)
return audio_segments
def get_alignments(self, table: pa.Table) -> Dict[str, pa.Array | pa.ChunkedArray]:
results = []
for dd in (
table.select(
[self._alignment_column.audio, self._alignment_column.segement_tokens]
)
.to_pandas()
.to_dict(orient="records")
):
struct = self.get_one_row_alignments(
dd[self._alignment_column.audio],
dd[self._alignment_column.segement_tokens],
)
results.append(struct)
batch = {}
segment_audio_bytes = self._alignment_column.segment_audio_bytes
batch[segment_audio_bytes] = pa.array(
[[seg[segment_audio_bytes] for seg in doc] for doc in results],
type=pa.list_(pa.large_list(pa.int8())),
)
segment_duration = self._alignment_column.segment_duration
batch[segment_duration] = pa.array(
[[seg[segment_duration] for seg in doc] for doc in results],
type=pa.list_(pa.float32()),
)
segment_start_sec = self._alignment_column.segment_start_sec
batch[segment_start_sec] = pa.array(
[[seg[segment_start_sec] for seg in doc] for doc in results],
type=pa.list_(pa.float32()),
)
gc.collect()
torch.cuda.empty_cache()
return batch
def __call__(self, table: pa.Table | None) -> pa.Table | None:
if table is None:
return table
batch = self.get_alignments(table)
for name, col in batch.items():
table = table.append_column(name, col) # type: ignore
return table
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