Add KWS example.

examples/hey_snips/README.md

@@ -1,22 +1,8 @@
-# MDTC Keyword Spotting with HeySnips Dataset
 
-## Dataset
+## Metrics
 
-Before running scripts, you **MUST** follow this instruction to download the dataset: https://github.com/sonos/keyword-spotting-research-datasets
+We mesure FRRs with fixing false alarms in one hour:
 
-After you download and decompress the dataset archive, you should **REPLACE** the value of `data_dir` in `conf/*.yaml` to complete dataset config.
+|Model|False Alarm| False Reject Rate|
-
+|--|--|--|
-## Get Started
+|MDTC| 1| 0.003559 |
-
-In this section, we will train the [MDTC](https://arxiv.org/pdf/2102.13552.pdf) model and evaluate on "Hey Snips" dataset.
-
-```sh
-CUDA_VISIBLE_DEVICES=0,1 ./run.sh conf/mdtc.yaml
-```
-
-This script contains training and scoring steps. You can just set the `CUDA_VISIBLE_DEVICES` environment var to run on single gpu or multi-gpus.
-
-The vars `stage` and `stop_stage` in `./run.sh` controls the running steps:
-- stage 1: Training from scratch.
-- stage 2: Evaluating model on test dataset and computing detection error tradeoff(DET) of all trigger thresholds.
-- stage 3: Plotting the DET cruve for visualizaiton.

examples/hey_snips/RESULTS.md

@@ -1,8 +0,0 @@
-
-## Metrics
-
-We mesure FRRs with fixing false alarms in one hour:
-
-|Model|False Alarm| False Reject Rate|
-|--|--|--|
-|MDTC| 1| 0.003559 |

examples/hey_snips/kws0/README.md

@@ -0,0 +1,22 @@
+# MDTC Keyword Spotting with HeySnips Dataset
+
+## Dataset
+
+Before running scripts, you **MUST** follow this instruction to download the dataset: https://github.com/sonos/keyword-spotting-research-datasets
+
+After you download and decompress the dataset archive, you should **REPLACE** the value of `data_dir` in `conf/*.yaml` to complete dataset config.
+
+## Get Started
+
+In this section, we will train the [MDTC](https://arxiv.org/pdf/2102.13552.pdf) model and evaluate on "Hey Snips" dataset.
+
+```sh
+CUDA_VISIBLE_DEVICES=0,1 ./run.sh conf/mdtc.yaml
+```
+
+This script contains training and scoring steps. You can just set the `CUDA_VISIBLE_DEVICES` environment var to run on single gpu or multi-gpus.
+
+The vars `stage` and `stop_stage` in `./run.sh` controls the running steps:
+- stage 1: Training from scratch.
+- stage 2: Evaluating model on test dataset and computing detection error tradeoff(DET) of all trigger thresholds.
+- stage 3: Plotting the DET cruve for visualizaiton.