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| author | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2021-05-20 11:18:53 +0100 |
|---|---|---|
| committer | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2021-05-20 12:57:49 +0000 |
| commit | e12ac836d2110403475d0e8b4bdfec03a0874f6c (patch) | |
| tree | 2f4ac0a379cd2cf6d5ad9bc11c2a0c14abb740c3 /docs/quick_start.md | |
| parent | 659fcd951ac18d1ee7737a6ddf6a3ec162c73ca5 (diff) | |
| download | ml-embedded-evaluation-kit-e12ac836d2110403475d0e8b4bdfec03a0874f6c.tar.gz | |
MLECO-1883: Updating wav2letter model
Using the new pruned wav2letter model from Arm Model Zoo.
The new model when optimised by Vela, produces a tflite
file ~10 MB smaller than the current.
Change-Id: I4ab6007c5b6111f41d8097e29b2af6cde2abc457
Diffstat (limited to 'docs/quick_start.md')
| -rw-r--r-- | docs/quick_start.md | 20 |
1 files changed, 9 insertions, 11 deletions
diff --git a/docs/quick_start.md b/docs/quick_start.md index abf8f50..6aea7b1 100644 --- a/docs/quick_start.md +++ b/docs/quick_start.md @@ -3,7 +3,7 @@ This is a quick start guide that will show you how to run the keyword spotting example application. The aim of this quick start guide is to enable you to run an application quickly on the Fixed Virtual Platform. The assumption we are making is that your Arm® Ethos™-U55 NPU is configured to use 128 Multiply-Accumulate units, -is using a shared SRAM with the Arm® Cortex®-M55. +is using a shared SRAM with the Arm® Cortex®-M55. 1. Verify you have installed [the required prerequisites](sections/building.md#Build-prerequisites). @@ -58,11 +58,11 @@ curl -L https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a --output ./resources_downloaded/ad/ifm0.npy curl -L https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/testing_output/Identity/0.npy \ --output ./resources_downloaded/ad/ofm0.npy -curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/wav2letter_int8.tflite \ - --output ./resources_downloaded/asr/wav2letter_int8.tflite -curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy \ +curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite \ + --output ./resources_downloaded/asr/wav2letter_pruned_int8.tflite +curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_2_int8/0.npy \ --output ./resources_downloaded/asr/ifm0.npy -curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_output/Identity_int8/0.npy \ +curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy \ --output ./resources_downloaded/asr/ofm0.npy curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/mobilenet_v2_1.0_224_quantized_1_default_1.tflite \ --output ./resources_downloaded/img_class/mobilenet_v2_1.0_224_quantized_1_default_1.tflite @@ -76,13 +76,11 @@ curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea --output ./resources_downloaded/kws/ifm0.npy curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy \ --output ./resources_downloaded/kws/ofm0.npy -curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/wav2letter_int8.tflite \ - --output ./resources_downloaded/kws_asr/wav2letter_int8.tflite -curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy \ +curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite \ + --output ./resources_downloaded/kws_asr/wav2letter_pruned_int8.tflite +curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_2_int8/0.npy \ --output ./resources_downloaded/kws_asr/asr/ifm0.npy -curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy - --output ./resources_downloaded/kws_asr/asr/ifm0.npy -curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_output/Identity_int8/0.npy \ +curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy \ --output ./resources_downloaded/kws_asr/asr/ofm0.npy curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite \ --output ./resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite |