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a7c2243 | 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 | # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import editdistance
import pytest
import torch
from omegaconf import OmegaConf
from tqdm.auto import tqdm
from nemo.collections.asr.models.aed_multitask_models import lens_to_mask
from nemo.collections.asr.parts.submodules.aed_decoding import (
GreedyBatchedStreamingAEDComputer,
return_decoder_input_ids,
)
from nemo.collections.asr.parts.submodules.multitask_decoding import (
AEDStreamingDecodingConfig,
MultiTaskDecodingConfig,
)
from nemo.collections.asr.parts.utils.manifest_utils import read_manifest
from nemo.collections.asr.parts.utils.streaming_utils import ContextSize
from tests.collections.asr.decoding.utils import load_audio, make_preprocessor_deterministic
DEVICES = [torch.device("cpu")]
if torch.cuda.is_available():
DEVICES.append(torch.device("cuda:0"))
if torch.mps.is_available():
DEVICES.append(torch.device("mps"))
def get_batch_encoder_outputs_from_records(records, model, device):
"""Helper function to get encoder outputs for a batch of manifest records"""
local_batch_size = len(records)
filenames = [record["audio_filepath"] for record in records]
audio_filepaths = filenames[:local_batch_size]
with torch.no_grad():
make_preprocessor_deterministic(model)
model.eval()
all_inputs, all_lengths = [], []
for audio_file in tqdm(audio_filepaths, desc="Loading audio files"):
audio_tensor, _ = load_audio(audio_file)
all_inputs.append(audio_tensor)
all_lengths.append(torch.tensor(audio_tensor.shape[0], dtype=torch.int64))
input_batch = torch.nn.utils.rnn.pad_sequence(all_inputs, batch_first=True).to(
device=device, dtype=torch.float32
)
length_batch = torch.tensor(all_lengths, dtype=torch.int64).to(device)
# get encoder output using full audio signal
_, encoded_length, encoded_output, _ = model(input_signal=input_batch, input_signal_length=length_batch)
return encoded_output, encoded_length
@pytest.mark.with_downloads
@pytest.mark.parametrize(
"device,use_cuda_graph_decoder",
[(device, False) for device in DEVICES] + [(device, True) for device in DEVICES if device.type == "cuda"],
)
@pytest.mark.parametrize("decoding_policy", ["waitk", "alignatt"])
@pytest.mark.parametrize("chunk_size", [3, 4])
@pytest.mark.parametrize("batch_size", [4])
def test_multi_task_streaming_decoding(
tmp_path_factory,
an4_val_manifest_corrected,
canary_180m_flash,
device: torch.device,
use_cuda_graph_decoder: bool,
decoding_policy: str,
chunk_size: int,
batch_size: int,
):
"""Test streaming decoding with multi-task model for different decoding policies"""
model = canary_180m_flash
model.eval()
model.to(device=device)
# setup streaming decoding config
streaming_decoding_cfg = AEDStreamingDecodingConfig()
streaming_decoding_cfg.streaming_policy = decoding_policy
streaming_decoding_cfg.chunk_secs = 1
streaming_decoding_cfg.right_context_secs = 0.0
streaming_decoding_cfg.batch_size = batch_size
streaming_decoding_cfg.prompt = OmegaConf.create({'pnc': 'no', 'task': 'asr'})
context_encoder_frames = ContextSize(
left=100,
chunk=chunk_size,
right=0.0,
)
# setup decoding strategy
if hasattr(model, 'change_decoding_strategy'):
multitask_decoding = MultiTaskDecodingConfig()
multitask_decoding.strategy = "greedy"
model.change_decoding_strategy(multitask_decoding)
manifest = read_manifest(an4_val_manifest_corrected)
all_hyps = []
tokens_frame_alignment = []
predicted_token_ids = []
decoding_computer = GreedyBatchedStreamingAEDComputer(
model,
frame_chunk_size=chunk_size,
decoding_cfg=streaming_decoding_cfg,
)
with torch.no_grad(), torch.inference_mode():
for i in range(0, len(manifest), batch_size):
encoder_output, encoder_output_len = get_batch_encoder_outputs_from_records(
manifest[i : i + batch_size], model=model, device=device
)
local_batch_size = encoder_output_len.shape[0]
decoder_input_ids = return_decoder_input_ids(streaming_decoding_cfg, model)
model_state = GreedyBatchedStreamingAEDComputer.initialize_aed_model_state(
asr_model=model,
decoder_input_ids=decoder_input_ids,
batch_size=local_batch_size,
context_encoder_frames=context_encoder_frames,
chunk_secs=streaming_decoding_cfg.chunk_secs,
right_context_secs=streaming_decoding_cfg.right_context_secs,
)
# decode encoder output by chunks, passing state between decoder invocations
for t in range(0, encoder_output.shape[1], chunk_size):
current_len = torch.full_like(encoder_output_len, fill_value=t + chunk_size)
current_len = torch.minimum(current_len, encoder_output_len)
model_state.is_last_chunk_batch = current_len >= encoder_output_len
encoder_input_mask = lens_to_mask(current_len, encoder_output[:, : t + chunk_size].shape[1]).to(
encoder_output.dtype
)
model_state = decoding_computer(
encoder_output=encoder_output[:, : t + chunk_size],
encoder_output_len=current_len,
encoder_input_mask=encoder_input_mask,
prev_batched_state=model_state,
)
# get final results for each sample in the batch
for j in range(local_batch_size):
transcription_idx = model_state.pred_tokens_ids[
j, model_state.decoder_input_ids.size(-1) : model_state.current_context_lengths[j]
]
transcription = model.tokenizer.ids_to_text(transcription_idx.tolist()).strip()
all_hyps.append(transcription)
tokens_frame_alignment.append(model_state.tokens_frame_alignment[j])
predicted_token_ids.append(
model_state.pred_tokens_ids[
j, model_state.decoder_input_ids.size(-1) : model_state.current_context_lengths[j]
]
)
# compare decoding results with reference transcripts
ref_transcripts = [item['text'] for item in manifest]
assert editdistance.eval(ref_transcripts, all_hyps) <= len(ref_transcripts) * 0.1 # Expected WER is less than 10%
# compute latency
audio_encoder_fs = 80 # in ms
laal_list = None
if decoding_policy == "waitk":
laal_list = decoding_computer.compute_waitk_lagging(
manifest, predicted_token_ids, context_encoder_frames, audio_encoder_fs, BOW_PREFIX="\u2581"
)
elif decoding_policy == "alignatt":
laal_list = decoding_computer.compute_alignatt_lagging(
manifest,
predicted_token_ids,
tokens_frame_alignment,
context_encoder_frames,
audio_encoder_fs,
BOW_PREFIX="\u2581",
)
else:
raise ValueError(f"Decoding policy {decoding_policy} is not supported")
laal = sum(laal_list) / len(laal_list)
assert 300 <= laal <= 900 # Expected LAAL is between 300ms and 900ms depending on the decoding policy
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