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#
# 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 os
import pytest
import torch
from lhotse import CutSet, SupervisionSegment
from lhotse.testing.dummies import dummy_cut, dummy_recording
from nemo.collections.common.data.utils import move_data_to_device
from nemo.collections.speechlm2.data import DuplexS2SDataset
from nemo.collections.speechlm2.models import DuplexS2SSpeechDecoderModel
if torch.cuda.is_available():
torch.set_default_device('cuda')
def resolve_pretrained_models():
if os.path.exists("/home/TestData/speechlm/pretrained_models"):
# CI pre-cached paths:
return {
"pretrained_llm": "/home/TestData/speechlm/pretrained_models/TinyLlama--TinyLlama_v1.1",
"pretrained_audio_codec": "/home/TestData/speechlm/pretrained_models/low-frame-rate-speech-codec-22khz.nemo",
"pretrained_asr": "/home/TestData/speechlm/pretrained_models/stt_en_fastconformer_hybrid_large_streaming_80ms.nemo",
"scoring_asr": "/home/TestData/speechlm/pretrained_models/stt_en_fastconformer_transducer_large.nemo",
}
else:
# HF URLs:
return {
"pretrained_asr": "stt_en_fastconformer_hybrid_large_streaming_80ms",
"scoring_asr": "stt_en_fastconformer_transducer_large",
"pretrained_llm": "TinyLlama/TinyLlama_v1.1",
"pretrained_audio_codec": "nvidia/low-frame-rate-speech-codec-22khz",
}
@pytest.fixture(scope="session")
def model():
cfg = {
**resolve_pretrained_models(),
"pretrained_weights": False,
"freeze_params": ["^audio_codec\\..+$"],
"audio_loss_weight": 1,
"text_loss_weight": 3,
"perception": {
"target": "nemo.collections.speechlm2.modules.perception.AudioPerceptionModule",
"output_dim": 2048,
"encoder": {
"_target_": "nemo.collections.asr.modules.ConformerEncoder",
"att_context_size": [-1, -1],
"causal_downsampling": False,
"conv_context_size": None,
"conv_kernel_size": 9,
"conv_norm_type": "batch_norm",
"d_model": 1024,
"dropout": 0.1,
"dropout_att": 0.1,
"dropout_emb": 0.0,
"dropout_pre_encoder": 0.1,
"feat_in": 128,
"feat_out": -1,
"ff_expansion_factor": 4,
"n_heads": 8,
"n_layers": 2,
"pos_emb_max_len": 5000,
"self_attention_model": "rel_pos",
"subsampling": "dw_striding",
"subsampling_conv_channels": 256,
"subsampling_factor": 8,
},
"modality_adapter": {
"_target_": "nemo.collections.speechlm2.modules.perception.IdentityConnector",
"d_model": 1024,
},
"preprocessor": {
"_target_": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
"dither": 1e-05,
"features": 128,
"frame_splicing": 1,
"log": True,
"n_fft": 512,
"normalize": "per_feature",
"pad_to": 0,
"pad_value": 0.0,
"sample_rate": 16000,
"window": "hann",
"window_size": 0.025,
"window_stride": 0.01,
},
},
"speech_decoder": {
"n_layers": 1,
"d_model": 768,
"d_ffn": 3072,
"sa_n_heads": 12,
"kernel_size": 3,
"is_causal": True,
},
"optimizer": {"_target_": "torch.optim.AdamW"},
}
model = DuplexS2SSpeechDecoderModel(cfg)
if torch.cuda.is_available():
model.to("cuda")
return model
@pytest.fixture(scope="session")
def dataset(model):
return DuplexS2SDataset(
model.tokenizer,
frame_length=0.08,
source_sample_rate=16000,
target_sample_rate=22050,
input_roles=["user"],
output_roles=["assistant"],
)
@pytest.fixture(scope="session")
def training_cutset_batch():
cut = dummy_cut(0, recording=dummy_recording(0, with_data=True))
cut.target_audio = dummy_recording(1, with_data=True)
cut.supervisions = [
SupervisionSegment(
id=cut.id,
recording_id=cut.recording_id,
start=0,
duration=0.1,
text='hi',
speaker="user",
),
SupervisionSegment(
id=cut.id,
recording_id=cut.recording_id,
start=0.3,
duration=0.1,
text='hello',
speaker="assistant",
),
SupervisionSegment(
id=cut.id,
recording_id=cut.recording_id,
start=0.5,
duration=0.1,
text='ok',
speaker="user",
),
SupervisionSegment(
id=cut.id,
recording_id=cut.recording_id,
start=0.6,
duration=0.4,
text='okay',
speaker="assistant",
),
]
return CutSet([cut])
def test_s2s_speech_decoder_training_step(model, dataset, training_cutset_batch):
model.on_train_epoch_start()
batch = dataset[training_cutset_batch]
batch = move_data_to_device(batch, device=model.device)
results = model.training_step(batch, batch_idx=0)
assert torch.is_tensor(results["loss"])
assert not torch.isnan(results["loss"])
assert results["loss"] > 0
def test_s2s_speech_decoder_validation_step(model, dataset, training_cutset_batch):
model.on_validation_epoch_start()
batch = dataset[training_cutset_batch]
batch = move_data_to_device(batch, device=model.device)
results = model.validation_step({"dummy_val_set": batch}, batch_idx=0)
assert results is None # no return value
def test_s2s_speech_decoder_offline_generation(model):
# 16000 samples == 1 second == 12.5 frames ~= 14 frames after encoder padding
ans = model.offline_inference(
input_signal=torch.randn(1, 16000, device=model.device),
input_signal_lens=torch.tensor([16000], device=model.device),
)
assert ans.keys() == {"text", "tokens_text", "tokens_audio", "audio", "audio_len", "tokens_len"}
assert isinstance(ans["text"], list)
assert isinstance(ans["text"][0], str)
gen_text = ans["tokens_text"]
assert gen_text.shape == (1, 13)
assert gen_text.dtype == torch.long
assert (gen_text >= 0).all()
assert (gen_text < model.text_vocab_size).all()
gen_audio_codes = ans["tokens_audio"]
assert gen_audio_codes.shape == (1, 13, 8)
assert gen_audio_codes.dtype == torch.long
assert (gen_audio_codes >= 0).all()
assert (gen_audio_codes < model.speech_vocab_size).all()
gen_audio = ans["audio"]
assert gen_audio.dtype == torch.float32
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