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cicd-main.yml
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# Copyright (c) 2020-2021, NVIDIA CORPORATION.
#
# 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.
name: "CICD NeMo"
on:
pull_request:
branches:
- "main"
- "r**"
- "weekly-bump"
types: [labeled]
push:
branches:
- main
workflow_dispatch:
inputs:
test_to_run:
required: false
default: all
type: string
description: Comma-separated list of tests to run. Use "all" to run the full test suite.
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
pre-flight:
runs-on: ubuntu-latest
outputs:
test_to_run: ${{ steps.test_to_run.outputs.main }}
build_args: ${{ steps.manifest.outputs.BUILD_ARGS }}
env:
TESTS_TO_RUN: ${{ inputs.test_to_run }}
EVENT_NAME: ${{ github.event_name }}
HAS_LABEL: ${{ github.event.label.name == 'Run CICD' }}
steps:
- name: Checkout branch
uses: actions/checkout@v4
- name: Select tests to run
id: test_to_run
run: |
# For manual dispatch, we replace `all` with the actual job names
if [[ "$EVENT_NAME" == "workflow_dispatch" && "$TESTS_TO_RUN" == "all" ]]; then
TESTS_TO_RUN=$(cat .github/workflows/cicd-main.yml | yq '.jobs | [to_entries[] | .key] | join(",")')
# For manual dispatch with provided list of tests, do nothing
elif [[ "$EVENT_NAME" == "workflow_dispatch" && "$TESTS_TO_RUN" != "all" ]]; then
TESTS_TO_RUN=$TESTS_TO_RUN
# For correctly labeled PR, we replace `all` with the actual job names
elif [[ "$EVENT_NAME" == "pull_request" && "$HAS_LABEL" == "true" ]]; then
TESTS_TO_RUN=$(cat .github/workflows/cicd-main.yml | yq '.jobs | [to_entries[] | .key] | join(",")')
# For incorrectly labeled PR, run no tests
elif [[ "$EVENT_NAME" == "pull_request" && "$HAS_LABEL" != "true" ]]; then
TESTS_TO_RUN=""
# For push events, run all tests. This is so that we can generate coverage
# on branch `main`.
elif [[ "$EVENT_NAME" == "push" ]]; then
TESTS_TO_RUN=$(cat .github/workflows/cicd-main.yml | yq '.jobs | [to_entries[] | .key] | join(",")')
else
echo "Unsupported event_name $EVENT_NAME provided".
exit 1
fi
parsed_string=$(echo "$TESTS_TO_RUN" | jq -c --raw-input 'split(",")')
echo "main=${parsed_string}" | tee -a "$GITHUB_OUTPUT"
- name: Parse manifest.json
id: manifest
run: |
BUILD_ARGS=$(cat << EOF
BASE_IMAGE=$(cat requirements/manifest.json | jq -r '."ngc-pytorch"')
MLM_REPO=$(cat requirements/manifest.json | jq -r '."vcs-dependencies"."megatron-lm".repo')
MLM_TAG=$(cat requirements/manifest.json | jq -r '."vcs-dependencies"."megatron-lm".ref')
TE_REPO=$(cat requirements/manifest.json | jq -r '."vcs-dependencies".transformer_engine.repo')
TE_TAG=$(cat requirements/manifest.json | jq -r '."vcs-dependencies".transformer_engine.ref')
APEX_REPO=$(cat requirements/manifest.json | jq -r '."vcs-dependencies".apex.repo')
APEX_TAG=$(cat requirements/manifest.json | jq -r '."vcs-dependencies".apex.ref')
EOF
)
echo "BUILD_ARGS<<EOF" >> $GITHUB_OUTPUT
echo "$BUILD_ARGS" >> $GITHUB_OUTPUT
echo "EOF" >> $GITHUB_OUTPUT
cicd-test-container-build:
if: ${{ needs.pre-flight.outputs.test_to_run != '' }}
uses: NVIDIA/NeMo-FW-CI-templates/.github/workflows/[email protected]
needs: pre-flight
with:
image-name: nemo_container
dockerfile: Dockerfile.ci
image-label: nemo-core
build-args: |
IMAGE_LABEL=nemo-core
NEMO_TAG=${{ github.sha }}
NEMO_REPO=https://github.com/NVIDIA/NeMo
${{ needs.pre-flight.outputs.BUILD_ARGS }}
prune-filter-timerange: 24h
cicd-import-tests:
if: ${{ needs.pre-flight.outputs.test_to_run != '' }}
needs: [cicd-test-container-build, pre-flight]
runs-on: self-hosted-azure-gpus-1
steps:
- name: Run some checks
run: |
docker run --rm --device=/dev/nvidia0 --gpus all --shm-size=8g --env TRANSFORMERS_OFFLINE=0 --env HYDRA_FULL_ERROR=1 --env PYTHONUNBUFFERED=1 nemoci.azurecr.io/nemo_container:${{ github.run_id }} bash -c '\
# PyTorch Lightning version
python -c "import lightning.pytorch; print(lightning.pytorch.__version__)"
# PyTorch Lightning DDP Checks
CUDA_VISIBLE_DEVICES="0,1" Gotchapython "tests/core_ptl/check_for_ranks.py"
# Basic Import Checks
python tests/core_ptl/check_imports.py --domain asr
python tests/core_ptl/check_imports.py --domain nlp
python tests/core_ptl/check_imports.py --domain tts '
# L0: GPU unit tests
L0_Unit_Tests_GPU_ASR:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_ASR')
with:
RUNNER: self-hosted-azure-gpus-1
TIMEOUT: 20
# TODO: remove this hack
SCRIPT: |
python -c "from nemo.collections.asr.models import ASRModel" && NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest tests/collections/asr -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_Audio:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_Audio')
with:
RUNNER: self-hosted-azure-gpus-1
TIMEOUT: 20
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest tests/collections/audio -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_Common:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_Common')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest tests/collections/common -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_LLM:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_LLM')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest tests/collections/llm -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_Multimodal:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_Multimodal')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest tests/collections/multimodal -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_TTS:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_TTS')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest tests/collections/tts -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_Core:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_Core')
with:
RUNNER: self-hosted-azure-gpus-1
TIMEOUT: 20
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest tests/core -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_Hydra:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_Hydra')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest tests/hydra -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_Lightning:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_Lightning')
with:
RUNNER: self-hosted-azure
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0,1 pytest tests/lightning -m "not pleasefixme" --with_downloads --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_GPU_Others:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_GPU_Others')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 CUDA_VISIBLE_DEVICES=0 pytest -m "not pleasefixme" --with_downloads \
--ignore=tests/collections/asr \
--ignore=tests/collections/audio \
--ignore=tests/collections/common \
--ignore=tests/collections/llm \
--ignore=tests/collections/multimodal \
--ignore=tests/collections/nlp \
--ignore=tests/collections/tts \
--ignore=tests/core \
--ignore=tests/core_ptl \
--ignore=tests/hydra \
--ignore=tests/lightning \
--ignore=tests/utils \
--cov-branch --cov-report=xml --cov=nemo
# L0: CPU unit tests
L0_Unit_Tests_CPU_ASR:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_ASR')
with:
RUNNER: self-hosted-azure-cpu
TIMEOUT: 20
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/collections/asr -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_Audio:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_Audio')
with:
RUNNER: self-hosted-azure-cpu
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/collections/audio -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_Common:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_Common')
with:
RUNNER: self-hosted-azure-cpu
TIMEOUT: 20
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/collections/common -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_LLM:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_LLM')
with:
RUNNER: self-hosted-azure-cpu
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/collections/llm -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_Multimodal:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_Multimodal')
with:
RUNNER: self-hosted-azure-cpu
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/collections/multimodal -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_TTS:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_TTS')
with:
RUNNER: self-hosted-azure-cpu
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/collections/tts -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_Core:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_Core')
with:
RUNNER: self-hosted-azure-cpu
TIMEOUT: 20
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/core tests/core_ptl -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_Hydra:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_Hydra')
with:
RUNNER: self-hosted-azure-cpu
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/hydra -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_Lightning:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_Lightning')
with:
RUNNER: self-hosted-azure-cpu
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest tests/lightning -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat --cov-branch --cov-report=xml --cov=nemo
L0_Unit_Tests_CPU_Others:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Unit_Tests_CPU_Others')
with:
RUNNER: self-hosted-azure-cpu
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat \
--ignore=tests/collections/asr \
--ignore=tests/collections/audio \
--ignore=tests/collections/common \
--ignore=tests/collections/llm \
--ignore=tests/collections/multimodal \
--ignore=tests/collections/nlp \
--ignore=tests/collections/tts \
--ignore=tests/core \
--ignore=tests/core_ptl \
--ignore=tests/hydra \
--ignore=tests/lightning \
--ignore=tests/utils \
--cov-branch --cov-report=xml --cov=nemo
L0_Setup_Test_Data_And_Models:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L0_Setup_Test_Data_And_Models')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python -m tests.setup --save_dir /home/TestData/nlp
# - name: L2: Multimodal Imagen Train
# L2: Community llava multimodal Checkpoints tests
L2_Community_vita_Checkpoints_tests_Llama3:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Community_vita_Checkpoints_tests_Llama3')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
mkdir /tmp/${{ github.run_id }}
export PYTHONPATH=/home/TestData/multimodal/video_neva/LLaVA:$PYTHONPATH
CUDA_VISIBLE_DEVICES=0 python examples/multimodal/multimodal_llm/neva/convert_llava_to_neva.py \
--in-file /home/TestData/multimodal/video_neva/Llama-3-VILA1.5-8B/llm \
--mm-projector-ckpt-dir /home/TestData/multimodal/video_neva/Llama-3-VILA1.5-8B/mm_projector \
--mm-vision-tower /home/TestData/multimodal/video_neva/Llama-3-VILA1.5-8B/vision_tower \
--tokenizer-model /home/TestData/multimodal/video_neva/vita-tokenizer/ \
--config-file vita_config.yaml \
--out-file=/tmp/${{ github.run_id }}/llama3_ci.nemo \
--model-type VITA \
--conv-template llama_3
# this test is using a 7B model which is too large for GitHub CI
# replace the model in this test with a toy model or move the test
# to the nightly CI
# OPTIONAL_L2_Community_LLM_Checkpoints_tests_Baichuan2:
# needs: [pre-flight, cicd-test-container-build]
# runs-on: self-hosted-azure
# container:
# image: nemoci.azurecr.io/nemo_container:${{ github.run_id }}
# options:
# # --user 0:128
# --device=/dev/nvidia0
# --gpus all
# --shm-size=8g
# --env TRANSFORMERS_OFFLINE=0
# --env HYDRA_FULL_ERROR=1
# --volume /mnt/datadrive/TestData:/home/TestData
# steps:
# - name: Checkout repository
# uses: actions/checkout@v4
# - run: |
# python scripts/checkpoint_converters/convert_baichuan2_hf_to_nemo.py \
# --input_name_or_path=/home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base \
# --output_path=/home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base/ci.nemo
# rm -f /home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base/ci.nemo
# - uses: "NVIDIA/NeMo/.github/actions/cancel-workflow@main"
# if: "failure()"
# L2: ASR dev run
ASR_dev_run_Speech_to_Text:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'ASR_dev_run_Speech_to_Text')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/asr_ctc/speech_to_text_ctc.py \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_to_text_results
ASR_dev_run_Speech_to_Text_WPE_-_CitriNet:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'ASR_dev_run_Speech_to_Text_WPE_-_CitriNet')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/asr_ctc/speech_to_text_ctc_bpe.py \
--config-path="../conf/citrinet/" --config-name="config_bpe" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
model.tokenizer.dir="/home/TestData/asr_tokenizers/an4_wpe_128/" \
model.tokenizer.type="wpe" \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_to_text_wpe_results
ASR_dev_run_Speech_Pre-training_-_CitriNet:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'ASR_dev_run_Speech_Pre-training_-_CitriNet')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/speech_pretraining/speech_pre_training.py \
--config-path="../conf/ssl/citrinet/" --config-name="citrinet_ssl_ci" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_pre_training_results
ASR_dev_run_Speech_To_Text_Finetuning:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'ASR_dev_run_Speech_To_Text_Finetuning')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/speech_to_text_finetune.py \
--config-path="conf/asr_finetune" --config-name="speech_to_text_finetune" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
init_from_nemo_model=/home/TestData/asr/stt_en_fastconformer_transducer_large.nemo \
model.tokenizer.update_tokenizer=False \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_finetuning_results
ASR_dev_run_Speech_To_Text_HF_Finetuning:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'ASR_dev_run_Speech_To_Text_HF_Finetuning')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |-
python examples/asr/speech_to_text_finetune.py \
--config-path="conf/asr_finetune" --config-name="speech_to_text_hf_finetune" \
~model.train_ds.hf_data_cfg \
model.train_ds.num_workers=1 \
model.train_ds.batch_size=2 model.validation_ds.batch_size=2 \
model.train_ds.streaming=true \
+model.train_ds.hf_data_cfg.path="librispeech_asr" \
+model.train_ds.hf_data_cfg.name=null \
+model.train_ds.hf_data_cfg.split="test.clean" \
+model.train_ds.hf_data_cfg.streaming=true \
+model.train_ds.hf_data_cfg.trust_remote_code=True \
~model.validation_ds.hf_data_cfg \
model.validation_ds.streaming=true \
+model.validation_ds.hf_data_cfg.path="librispeech_asr" \
+model.validation_ds.hf_data_cfg.name=null \
+model.validation_ds.hf_data_cfg.split="test.clean" \
+model.validation_ds.hf_data_cfg.streaming=true \
+model.validation_ds.hf_data_cfg.trust_remote_code=True \
~model.test_ds \
init_from_nemo_model=/home/TestData/asr/stt_en_fastconformer_transducer_large.nemo \
model.tokenizer.update_tokenizer=False \
model.optim.sched.warmup_steps=0 \
+model.optim.sched.max_steps=3 \
trainer.max_epochs=null \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_finetuning_results
ASR_dev_run_Speech_to_Text_WPE_-_Conformer:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'ASR_dev_run_Speech_to_Text_WPE_-_Conformer')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/asr_ctc/speech_to_text_ctc_bpe.py \
--config-path="../conf/conformer" --config-name="conformer_ctc_bpe" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
model.tokenizer.dir="/home/TestData/asr_tokenizers/an4_wpe_128/" \
model.tokenizer.type="wpe" \
model.train_ds.batch_size=4 \
model.validation_ds.batch_size=4 \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_to_text_wpe_conformer_results
# L2: ASR dev run - part two
ASR_dev_run-part_two_Speech_to_Text_WPE_-_Squeezeformer:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'ASR_dev_run-part_two_Speech_to_Text_WPE_-_Squeezeformer')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/asr_ctc/speech_to_text_ctc_bpe.py \
--config-path="../conf/squeezeformer" --config-name="squeezeformer_ctc_bpe" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
model.tokenizer.dir="/home/TestData/asr_tokenizers/an4_wpe_128/" \
model.tokenizer.type="wpe" \
model.encoder.d_model=144 \
model.train_ds.batch_size=4 \
model.validation_ds.batch_size=4 \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_to_text_wpe_squeezeformer_results
L2_Speech_to_Text_EMA:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speech_to_Text_EMA')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/asr/asr_ctc/speech_to_text_ctc.py \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
trainer.devices=2 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
+exp_manager.ema.enable=True \
exp_manager.exp_dir=/tmp/speech_to_text_results
L2_Speech_to_Text_AED:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speech_to_Text_AED')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/speech_multitask/speech_to_text_aed.py \
model.prompt_format=canary \
model.model_defaults.asr_enc_hidden=256 \
model.model_defaults.lm_dec_hidden=256 \
model.encoder.n_layers=12 \
model.transf_encoder.num_layers=0 \
model.transf_decoder.config_dict.num_layers=12 \
model.train_ds.manifest_filepath=/home/TestData/asr/manifests/canary/an4_canary_train.json \
model.train_ds.batch_duration=60 \
model.train_ds.use_bucketing=false \
model.train_ds.shuffle_buffer_size=100 \
model.train_ds.num_workers=0 \
+model.train_ds.text_field="answer" \
+model.train_ds.lang_field="target_lang" \
model.validation_ds.manifest_filepath=/home/TestData/asr/manifests/canary/an4_canary_val.json \
+model.validation_ds.text_field="answer" \
+model.validation_ds.lang_field="target_lang" \
model.validation_ds.num_workers=0 \
model.test_ds.manifest_filepath=/home/TestData/asr/manifests/canary/an4_canary_val.json \
+model.test_ds.text_field="answer" \
+model.test_ds.lang_field="target_lang" \
model.test_ds.num_workers=0 \
spl_tokens.model_dir=/home/TestData/asr_tokenizers/canary/canary_spl_tokenizer_v32 \
model.tokenizer.langs.en.dir=/home/TestData/asr_tokenizers/canary/en/tokenizer_spe_bpe_v1024_max_4 \
model.tokenizer.langs.en.type=bpe \
++model.tokenizer.langs.es.dir=/home/TestData/asr_tokenizers/canary/es/tokenizer_spe_bpe_v1024_max_4 \
++model.tokenizer.langs.es.type=bpe \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_to_text_aed_results
# L2: Speaker dev run
L2_Speaker_dev_run_Speaker_Recognition:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_Speaker_Recognition')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/speaker_tasks/recognition/speaker_reco.py \
model.train_ds.batch_size=10 \
model.validation_ds.batch_size=2 \
model.train_ds.manifest_filepath=/home/TestData/an4_speaker/train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_speaker/dev.json \
model.decoder.num_classes=2 \
trainer.max_epochs=10 \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speaker_recognition_results
L2_Speaker_dev_run_Speaker_Diarization:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_Speaker_Diarization')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder.py \
model.diarizer.speaker_embeddings.model_path=titanet_large \
model.train_ds.batch_size=5 \
model.validation_ds.batch_size=5 \
model.train_ds.emb_dir=examples/speaker_tasks/diarization/speaker_diarization_results \
model.validation_ds.emb_dir=examples/speaker_tasks/diarization/speaker_diarization_results \
model.train_ds.manifest_filepath=/home/TestData/an4_diarizer/simulated_train/msdd_data.50step.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_diarizer/simulated_valid/msdd_data.50step.json \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speaker_diarization_results
L2_Speaker_dev_run_EndtoEnd_Speaker_Diarization_Sortformer:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_EndtoEnd_Speaker_Diarization_Sortformer')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/speaker_tasks/diarization/neural_diarizer/sortformer_diar_train.py \
trainer.devices="[0]" \
batch_size=3 \
model.train_ds.manifest_filepath=/home/TestData/an4_diarizer/simulated_train/eesd_train_tiny.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_diarizer/simulated_valid/eesd_valid_tiny.json \
exp_manager.exp_dir=/tmp/speaker_diarization_results \
+trainer.fast_dev_run=True
L2_Speaker_dev_run_EndtoEnd_Diarizer_Inference:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_EndtoEnd_Diarizer_Inference')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/speaker_tasks/diarization/neural_diarizer/e2e_diarize_speech.py \
model_path=/home/TestData/an4_diarizer/diar_sortformer_4spk-v1-tiny.nemo \
dataset_manifest=/home/TestData/an4_diarizer/simulated_valid/eesd_valid_tiny.json \
batch_size=1
L2_Speaker_dev_run_Speech_to_Label:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_Speech_to_Label')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/speech_classification/speech_to_label.py \
model.train_ds.manifest_filepath=/home/TestData/speech_commands/train_manifest.json \
model.validation_ds.manifest_filepath=/home/TestData/speech_commands/test_manifest.json \
model.test_ds.manifest_filepath=/home/TestData/speech_commands/test_manifest.json \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
model.preprocessor._target_=nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor \
~model.preprocessor.window_size \
~model.preprocessor.window_stride \
~model.preprocessor.window \
~model.preprocessor.n_mels \
~model.preprocessor.n_mfcc \
~model.preprocessor.n_fft \
exp_manager.exp_dir=/tmp/speech_to_label_results
L2_Speaker_dev_run_Speaker_Diarization_with_ASR_Inference:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_Speaker_Diarization_with_ASR_Inference')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_with_asr_infer.py \
diarizer.manifest_filepath=/home/TestData/an4_diarizer/an4_manifest.json \
diarizer.speaker_embeddings.model_path=/home/TestData/an4_diarizer/spkr.nemo \
diarizer.speaker_embeddings.parameters.save_embeddings=True \
diarizer.speaker_embeddings.parameters.window_length_in_sec=[1.5] \
diarizer.speaker_embeddings.parameters.shift_length_in_sec=[0.75] \
diarizer.speaker_embeddings.parameters.multiscale_weights=[1.0] \
diarizer.asr.model_path=QuartzNet15x5Base-En \
diarizer.asr.parameters.asr_based_vad=True \
diarizer.out_dir=/tmp/speaker_diarization_asr_results
L2_Speaker_dev_run_Clustering_Diarizer_Inference:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_Clustering_Diarizer_Inference')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_infer.py \
diarizer.manifest_filepath=/home/TestData/an4_diarizer/an4_manifest.json \
diarizer.speaker_embeddings.model_path=/home/TestData/an4_diarizer/spkr.nemo \
diarizer.speaker_embeddings.parameters.save_embeddings=True \
diarizer.speaker_embeddings.parameters.window_length_in_sec=1.5 \
diarizer.speaker_embeddings.parameters.shift_length_in_sec=0.75 \
diarizer.speaker_embeddings.parameters.multiscale_weights=null \
diarizer.vad.model_path=/home/TestData/an4_diarizer/MatchboxNet_VAD_3x2.nemo \
diarizer.out_dir=/tmp/clustering_diarizer_results
L2_Speaker_dev_run_Neural_Diarizer_Inference:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_Neural_Diarizer_Inference')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder_infer.py \
diarizer.manifest_filepath=/home/TestData/an4_diarizer/an4_manifest.json \
diarizer.msdd_model.model_path=/home/TestData/an4_diarizer/diar_msdd_telephonic.nemo \
diarizer.speaker_embeddings.parameters.save_embeddings=True \
diarizer.vad.model_path=/home/TestData/an4_diarizer/MatchboxNet_VAD_3x2.nemo \
diarizer.out_dir=/tmp/neural_diarizer_results
L2_Speaker_dev_run_Multispeaker_ASR_Data_Simulation:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speaker_dev_run_Multispeaker_ASR_Data_Simulation')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python tools/speech_data_simulator/multispeaker_simulator.py \
--config-path=conf --config-name=data_simulator.yaml \
data_simulator.random_seed=42 \
data_simulator.manifest_filepath=/home/TestData/LibriSpeechShort/dev-clean-align-short.json \
data_simulator.outputs.output_dir=/tmp/test_simulator \
data_simulator.session_config.num_sessions=2 \
data_simulator.session_config.session_length=60
# L2: ASR Multi-dataloader dev run
L2_ASR_Multi-dataloader_dev_run_Speech_to_Text_multi-dataloader:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_ASR_Multi-dataloader_dev_run_Speech_to_Text_multi-dataloader')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/asr_ctc/speech_to_text_ctc.py \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=[/home/TestData/an4_dataset/an4_val.json,/home/TestData/an4_dataset/an4_val.json] \
trainer.devices=1 \
trainer.accelerator="gpu" \
trainer.max_epochs=1 \
trainer.max_steps=1 \
+trainer.num_sanity_val_steps=1 \
exp_manager.exp_dir=/tmp/speech_to_text_results
L2_ASR_Multi-dataloader_dev_run_Speech_to_Label_multi-dataloader:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_ASR_Multi-dataloader_dev_run_Speech_to_Label_multi-dataloader')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/speech_classification/speech_to_label.py \
model.train_ds.manifest_filepath=/home/TestData/speech_commands/train_manifest.json \
model.validation_ds.manifest_filepath=[/home/TestData/speech_commands/test_manifest.json,/home/TestData/speech_commands/test_manifest.json] \
trainer.devices=1 \
trainer.accelerator="gpu" \
trainer.max_epochs=1 \
trainer.max_steps=1 \
+trainer.num_sanity_val_steps=1 \
model.preprocessor._target_=nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor \
~model.preprocessor.window_size \
~model.preprocessor.window_stride \
~model.preprocessor.window \
~model.preprocessor.n_mels \
~model.preprocessor.n_mfcc \
~model.preprocessor.n_fft \
exp_manager.exp_dir=/tmp/speech_to_label_results
# L2: ASR Adapters
L2_ASR_Adapters_Linear_Adapters:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_ASR_Adapters_Linear_Adapters')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/asr_adapters/train_asr_adapter.py \
model.pretrained_model="stt_en_conformer_ctc_small" \
model.adapter.adapter_name="an4" \
model.adapter.linear.in_features=176 \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
trainer.max_steps=5 \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_to_text_adapters_results
L2_ASR_Adapters_RelPos_MHA_Adapters:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_ASR_Adapters_RelPos_MHA_Adapters')
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
python examples/asr/asr_adapters/train_asr_adapter.py \
model.pretrained_model="stt_en_conformer_ctc_small" \
model.adapter.adapter_name="encoder:an4" \
model.adapter.adapter_type="tiny_attn" \
model.adapter.tiny_attn.n_feat=176 \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
trainer.max_steps=5 \
trainer.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=/tmp/speech_to_text_adapters_mha_results
# L2: OOMptimizer
L2_Speech_Estimate_Duration_Bins:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speech_Estimate_Duration_Bins')
with:
RUNNER: self-hosted-azure
SCRIPT: |
set -x
# 1D buckets [SSL, CTC]
python scripts/speech_recognition/estimate_duration_bins.py \
/home/TestData/an4_dataset/an4_train.json \
--buckets 5
# 2D buckets [CTC, RNNT, TDT] / with tokenizer
python scripts/speech_recognition/estimate_duration_bins_2d.py \
/home/TestData/an4_dataset/an4_train_lang.json \
--tokenizer /home/TestData/asr_tokenizers/canary/en/tokenizer_spe_bpe_v1024_max_4/tokenizer.model \
--buckets 5 \
--sub-buckets 2
# TODO(pzelasko): Figure out how to quote the value in the test properly for CI to accept it...
# 2D buckets with prompt [AED/Canary, SpeechLM] / with aggregate tokenizer + prompt format
# python scripts/speech_recognition/estimate_duration_bins_2d.py \
# /home/TestData/an4_dataset/an4_train_lang.json \
# --tokenizer /home/TestData/asr_tokenizers/canary/canary_spl_tokenizer_v32/tokenizer.model \
# /home/TestData/asr_tokenizers/canary/en/tokenizer_spe_bpe_v1024_max_4/tokenizer.model \
# /home/TestData/asr_tokenizers/canary/es/tokenizer_spe_bpe_v1024_max_4/tokenizer.model \
# --langs spl_tokens en es \
# --prompt-format canary \
# --prompt '[{"role":"user","slots":{"source_lang":"en","target_lang":"en","task":"asr","pnc":"yes"}}]' \
# --buckets 5 \
# --sub-buckets 2
# L2: OOMptimizer
L2_Speech_Batch_Size_OOMptimizer:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speech_Batch_Size_OOMptimizer')
with:
RUNNER: self-hosted-azure
SCRIPT: |
# 1D bucketing
python scripts/speech_recognition/oomptimizer.py \
-c /home/TestData/oomptimizer/fast-conformer_ctc_bpe.yaml \
-m nemo.collections.asr.models.EncDecCTCModelBPE \
-b "[5.0,10.0]"
# 2D bucketing
python scripts/speech_recognition/oomptimizer.py \
-c /home/TestData/oomptimizer/fast-conformer_ctc_bpe.yaml \
-m nemo.collections.asr.models.EncDecCTCModelBPE \
-b "[[5.0,30],[5.0,45],[10.0,57],[10.0,71]]"
# L2: OOMptimizer Canary (has a different batch schema)
Optional_L2_Speech_Batch_Size_OOMptimizer_Canary:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'Optional_L2_Speech_Batch_Size_OOMptimizer_Canary')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python scripts/speech_recognition/oomptimizer.py \
-c /home/TestData/oomptimizer/fast-conformer_aed.yaml \
-m nemo.collections.asr.models.EncDecMultiTaskModel \
-b "[[5.0,30],[5.0,45],[10.0,57],[10.0,71]]"
IS_OPTIONAL: true
# L2: Speech Transcription
L2_Speech_Transcription_Speech_to_Text_Transcribe:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speech_Transcription_Speech_to_Text_Transcribe')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/asr/transcribe_speech.py \
pretrained_name="QuartzNet15x5Base-En" \
audio_dir="/home/TestData/an4_transcribe/test_subset/" \
output_filename="/tmp/stt_test_res.json" \
amp=true
# L2: Speech Transcription
L2_Speech_Transcription_Canary_Transcribe_Full_Manifest:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speech_Transcription_Canary_Transcribe_Full_Manifest')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/asr/transcribe_speech.py \
dataset_manifest=/home/TestData/asr/canary/dev-other-wav-10-canary-fields.json \
output_filename=/tmp/preds.json \
batch_size=10 \
pretrained_name=nvidia/canary-1b \
num_workers=0 \
amp=false \
compute_dtype=bfloat16 \
matmul_precision=medium
AFTER_SCRIPT: |
rm -rf /tmp/preds.json transcribe.log
L2_Speech_Transcription_Canary_Transcribe_With_Prompt:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speech_Transcription_Canary_Transcribe_With_Prompt')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/asr/transcribe_speech.py \
dataset_manifest=/home/TestData/asr/canary/dev-other-wav-10.json \
output_filename=preds.json \
batch_size=10 \
pretrained_name=nvidia/canary-1b \
num_workers=0 \
amp=false \
compute_dtype=bfloat16 \
matmul_precision=medium \
+prompt.source_lang="en" \
+prompt.target_lang="en" \
+prompt.task="asr" \
+prompt.pnc="no"
AFTER_SCRIPT: |
rm -rf preds.json transcribe.log
L2_Speech_Transcription_Canary_Transcribe_Audio_Dir:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Speech_Transcription_Canary_Transcribe_Audio_Dir')
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/asr/transcribe_speech.py \
audio_dir=/home/TestData/asr/canary/dev-other-wav \
output_filename=preds.json \
batch_size=10 \
pretrained_name=nvidia/canary-1b \
num_workers=0 \
amp=false \
compute_dtype=bfloat16 \
matmul_precision=medium
AFTER_SCRIPT: |
rm -rf preds.json
IS_OPTIONAL: true
# L2: Segmentation Tool
L2_Segmentation_Tool_Parallel_ctc_segmentation_test_L2_Eng_CitriNet_with_wav:
needs: [pre-flight, cicd-test-container-build]
uses: ./.github/workflows/_test_template.yml
if: contains(fromJSON(needs.pre-flight.outputs.test_to_run), 'L2_Segmentation_Tool_Parallel_ctc_segmentation_test_L2_Eng_CitriNet_with_wav')
with:
RUNNER: self-hosted-azure
SCRIPT: |
cd tools/ctc_segmentation && \
TIME=`date +"%Y-%m-%d-%T"` && \
/bin/bash run_segmentation.sh \
--MODEL_NAME_OR_PATH="stt_en_citrinet_512_gamma_0_25" \
--DATA_DIR=/home/TestData/ctc_segmentation/eng \
--OUTPUT_DIR=/tmp/ctc_seg_en/output${TIME} \