mirror of
https://github.com/Monadical-SAS/reflector.git
synced 2025-12-20 20:29:06 +00:00
fix: optimize parakeet transcription batching algorithm (#577)
* refactor: optimize transcription batching to accumulate speech segments - Changed VAD segment generator to return full audio array instead of segments - Removed segment filtering step - Modified batch_segments to accumulate maximum speech including silence - Transcribe larger continuous chunks instead of individual speech segments * fix: correct transcribe_batch call to use list and fix batch unpacking * fix: simplify * fix: remove unused variables * fix: add typing
This commit is contained in:
GitHub
@@ -3,7 +3,7 @@ import os
|
||||
import sys
|
||||
import threading
|
||||
import uuid
|
||||
from typing import Mapping, NewType
|
||||
from typing import Generator, Mapping, NewType
|
||||
from urllib.parse import urlparse
|
||||
|
||||
import modal
|
||||
@@ -14,10 +14,7 @@ SAMPLERATE = 16000
|
||||
UPLOADS_PATH = "/uploads"
|
||||
CACHE_PATH = "/cache"
|
||||
VAD_CONFIG = {
|
||||
"max_segment_duration": 30.0,
|
||||
"batch_max_files": 10,
|
||||
"batch_max_duration": 5.0,
|
||||
"min_segment_duration": 0.02,
|
||||
"batch_max_duration": 30.0,
|
||||
"silence_padding": 0.5,
|
||||
"window_size": 512,
|
||||
}
|
||||
@@ -271,7 +268,9 @@ class TranscriberParakeetFile:
|
||||
audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
|
||||
return audio_array
|
||||
|
||||
def vad_segment_generator(audio_array):
|
||||
def vad_segment_generator(
|
||||
audio_array,
|
||||
) -> Generator[tuple[float, float], None, None]:
|
||||
"""Generate speech segments using VAD with start/end sample indices"""
|
||||
vad_iterator = VADIterator(self.vad_model, sampling_rate=SAMPLERATE)
|
||||
window_size = VAD_CONFIG["window_size"]
|
||||
@@ -297,76 +296,65 @@ class TranscriberParakeetFile:
|
||||
start_time = start / float(SAMPLERATE)
|
||||
end_time = end / float(SAMPLERATE)
|
||||
|
||||
# Extract the actual audio segment
|
||||
audio_segment = audio_array[start:end]
|
||||
|
||||
yield (start_time, end_time, audio_segment)
|
||||
yield (start_time, end_time)
|
||||
start = None
|
||||
|
||||
vad_iterator.reset_states()
|
||||
|
||||
def vad_segment_filter(segments):
|
||||
"""Filter VAD segments by duration and chunk large segments"""
|
||||
min_dur = VAD_CONFIG["min_segment_duration"]
|
||||
max_dur = VAD_CONFIG["max_segment_duration"]
|
||||
def batch_speech_segments(
|
||||
segments: Generator[tuple[float, float], None, None], max_duration: int
|
||||
) -> Generator[tuple[float, float], None, None]:
|
||||
"""
|
||||
Input segments:
|
||||
[0-2] [3-5] [6-8] [10-11] [12-15] [17-19] [20-22]
|
||||
|
||||
for start_time, end_time, audio_segment in segments:
|
||||
segment_duration = end_time - start_time
|
||||
↓ (max_duration=10)
|
||||
|
||||
# Skip very small segments
|
||||
if segment_duration < min_dur:
|
||||
Output batches:
|
||||
[0-8] [10-19] [20-22]
|
||||
|
||||
Note: silences are kept for better transcription, previous implementation was
|
||||
passing segments separatly, but the output was less accurate.
|
||||
"""
|
||||
batch_start_time = None
|
||||
batch_end_time = None
|
||||
|
||||
for start_time, end_time in segments:
|
||||
if batch_start_time is None or batch_end_time is None:
|
||||
batch_start_time = start_time
|
||||
batch_end_time = end_time
|
||||
continue
|
||||
|
||||
# If segment is within max duration, yield as-is
|
||||
if segment_duration <= max_dur:
|
||||
yield (start_time, end_time, audio_segment)
|
||||
total_duration = end_time - batch_start_time
|
||||
|
||||
if total_duration <= max_duration:
|
||||
batch_end_time = end_time
|
||||
continue
|
||||
|
||||
# Chunk large segments into smaller pieces
|
||||
chunk_samples = int(max_dur * SAMPLERATE)
|
||||
current_start = start_time
|
||||
yield (batch_start_time, batch_end_time)
|
||||
batch_start_time = start_time
|
||||
batch_end_time = end_time
|
||||
|
||||
for chunk_offset in range(0, len(audio_segment), chunk_samples):
|
||||
chunk_audio = audio_segment[
|
||||
chunk_offset : chunk_offset + chunk_samples
|
||||
]
|
||||
if len(chunk_audio) == 0:
|
||||
break
|
||||
if batch_start_time is None or batch_end_time is None:
|
||||
return
|
||||
|
||||
chunk_duration = len(chunk_audio) / float(SAMPLERATE)
|
||||
chunk_end = current_start + chunk_duration
|
||||
yield (batch_start_time, batch_end_time)
|
||||
|
||||
# Only yield chunks that meet minimum duration
|
||||
if chunk_duration >= min_dur:
|
||||
yield (current_start, chunk_end, chunk_audio)
|
||||
def batch_segment_to_audio_segment(segments, audio_array):
|
||||
for start_time, end_time in segments:
|
||||
start_sample = int(start_time * SAMPLERATE)
|
||||
end_sample = int(end_time * SAMPLERATE)
|
||||
audio_segment = audio_array[start_sample:end_sample]
|
||||
|
||||
current_start = chunk_end
|
||||
|
||||
def batch_segments(segments, max_files=10, max_duration=5.0):
|
||||
batch = []
|
||||
batch_duration = 0.0
|
||||
|
||||
for start_time, end_time, audio_segment in segments:
|
||||
segment_duration = end_time - start_time
|
||||
|
||||
if segment_duration < VAD_CONFIG["silence_padding"]:
|
||||
if end_time - start_time < VAD_CONFIG["silence_padding"]:
|
||||
silence_samples = int(
|
||||
(VAD_CONFIG["silence_padding"] - segment_duration) * SAMPLERATE
|
||||
(VAD_CONFIG["silence_padding"] - (end_time - start_time))
|
||||
* SAMPLERATE
|
||||
)
|
||||
padding = np.zeros(silence_samples, dtype=np.float32)
|
||||
audio_segment = np.concatenate([audio_segment, padding])
|
||||
segment_duration = VAD_CONFIG["silence_padding"]
|
||||
|
||||
batch.append((start_time, end_time, audio_segment))
|
||||
batch_duration += segment_duration
|
||||
|
||||
if len(batch) >= max_files or batch_duration >= max_duration:
|
||||
yield batch
|
||||
batch = []
|
||||
batch_duration = 0.0
|
||||
|
||||
if batch:
|
||||
yield batch
|
||||
yield start_time, end_time, audio_segment
|
||||
|
||||
def transcribe_batch(model, audio_segments):
|
||||
with NoStdStreams():
|
||||
@@ -376,8 +364,6 @@ class TranscriberParakeetFile:
|
||||
def emit_results(
|
||||
results,
|
||||
segments_info,
|
||||
batch_index,
|
||||
total_batches,
|
||||
):
|
||||
"""Yield transcribed text and word timings from model output, adjusting timestamps to absolute positions."""
|
||||
for i, (output, (start_time, end_time, _)) in enumerate(
|
||||
@@ -413,35 +399,26 @@ class TranscriberParakeetFile:
|
||||
all_words = []
|
||||
|
||||
raw_segments = vad_segment_generator(audio_array)
|
||||
filtered_segments = vad_segment_filter(raw_segments)
|
||||
batches = batch_segments(
|
||||
filtered_segments,
|
||||
VAD_CONFIG["batch_max_files"],
|
||||
speech_segments = batch_speech_segments(
|
||||
raw_segments,
|
||||
VAD_CONFIG["batch_max_duration"],
|
||||
)
|
||||
audio_segments = batch_segment_to_audio_segment(speech_segments, audio_array)
|
||||
|
||||
batch_index = 0
|
||||
total_batches = max(
|
||||
1, int(total_duration / VAD_CONFIG["batch_max_duration"]) + 1
|
||||
)
|
||||
|
||||
for batch in batches:
|
||||
batch_index += 1
|
||||
audio_segments = [seg[2] for seg in batch]
|
||||
results = transcribe_batch(self.model, audio_segments)
|
||||
for batch in audio_segments:
|
||||
_, _, audio_segment = batch
|
||||
results = transcribe_batch(self.model, [audio_segment])
|
||||
|
||||
for text, words in emit_results(
|
||||
results,
|
||||
batch,
|
||||
batch_index,
|
||||
total_batches,
|
||||
[batch],
|
||||
):
|
||||
if not text:
|
||||
continue
|
||||
all_text_parts.append(text)
|
||||
all_words.extend(words)
|
||||
|
||||
processed_duration += sum(len(seg[2]) / float(SAMPLERATE) for seg in batch)
|
||||
processed_duration += len(audio_segment) / float(SAMPLERATE)
|
||||
|
||||
combined_text = " ".join(all_text_parts)
|
||||
return {"text": combined_text, "words": all_words}
|
||||
|
||||
Reference in New Issue
Block a user