Added a README with training instructions

README.md

@@ -5,3 +5,47 @@ license: cc0-1.0
 This is a processed LibriLight dataset ready for training the WhisperSpeech models.
 
 See [https://github.com/collabora/WhisperSpeech](https://github.com/collabora/WhisperSpeech) for more details.
+
+## Quick start
+
+If you want to quickly train a basic WhisperSpeech model you can start by downloading the small subset:
+
+```bash
+# magic includes to download only the small and validation data splits and the accompanying config files
+huggingface-cli download --repo-type dataset --include '*-small-*' '*small.dataset' '*-speakers*' --local-dir . -- collabora/whisperspeech-librilight
+
+# download the semantic token model to extract the token embeddings from it
+huggingface-cli download collabora/whisperspeech whisper-vq-stoks-medium-en+pl.model
+
+# the T2S training invocation:
+python3 -m whisperspeech.train_multi \
+  --task "t2s_up_wds_mlang_enclm base --frozen_embeddings_model whisper-vq-stoks-medium-en+pl.model" \
+  --batch-size 32 --accumulate-grad-batches 2 \
+  --epochs 2 --lr-schedule wsd \
+  --tunables="--cps_input --causal_encoder --warmup_steps=300 --encoder_depth_ratio=.25" \
+  --dataset-config=--vq_codes=513 \
+  --training-data @librilight-t2s-train-small.dataset \
+  --validation-data @librilight-t2s-val-common-speakers.dataset \
+  --validation-data @librilight-t2s-val-unseen-speakers.dataset \
+  --monitored-metric 'val_loss/dataloader_idx_0'
+
+# the S2A training invocation:
+python3 -m whisperspeech.train_multi \
+  --task "s2a_delar_mup_wds_mlang tiny --quantizers 4 --spk_width=192 --frozen_embeddings_model whisper-vq-stoks-medium-en+pl.model" \
+  --batch-size 48 \
+  --epochs 4 --lr-schedule wsd \
+  --tunables="--rope --warmup_steps=300" \
+  --dataset-config=--vq_codes=513 \
+  --training-data @librilight-s2a-train-small.dataset \
+  --validation-data @librilight-s2a-val-common-speakers.dataset \
+  --validation-data @librilight-s2a-val-unseen-speakers.dataset \
+  --monitored-metric 'val_loss/dataloader_idx_0'
+```
+
+The `--accumulate-grad-batches` option is set to get a good effective batch size a single 4090 GPU.
+If you have multiple GPUs it will probably make sense to lower the batch size. For example 16 GPUs
+with a batch size of 16 seem to be give good performance and fast training.
+
+Because we use Maximum Update Parametrization, higher effective batch sizes always result in lower
+losses and you don't need to adjust the learning rate. Unfortunately the effect is not linear so
+there is an optimal batch size and there is little benefit to increase it further.