some changes: plot folders, data folder structure, ...

local/classification_kmeans.py

@@ -115,7 +115,7 @@ def main(X_path, y_path, pcaA_path, pcab_path, out_path, json_path, NC):
         kmeans = faiss.Kmeans(
             d,
             int(NC),
-            niter=10,
+            niter=100,
             verbose=True,
             gpu=False,
         )

local/codebook_freqs.py

@@ -35,12 +35,14 @@ def calc_codebook_indexes(audio_path, freq, N):
         N = N + Nwav
         print('feature vectors: {}/{} (file/all)'.format(Nwav, N))
     else:
+        Nwav = 0
         print('WARNING: Input size of file is {} (smaller than Kernel size), skip ...'.format(len(x)))
     return freq, Nwav
 
 def main(exp_path, lst_path, json_path, model_path, VERBOSE):
     # set model and device global
     global model, device
+    #device = torch.device('cpu')
     device = torch.device('cuda')
     # load existing model
     model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([model_path])
@@ -72,13 +74,14 @@ def main(exp_path, lst_path, json_path, model_path, VERBOSE):
                 rows = tsv.readlines()
                 for row in rows:
                     uttID, audio_path = row.split()
-                    corpus_match = ('_').join(audio_path.split('/')[1].split('_')[1:]) # DATA/data_corpus_speakingstyle/spk/*wav
-                    spk_match = audio_path.split('/')[2] # DATA/data_corpus_speakingstyle/spk/*wav
+                    corpus_match = ('_').join(audio_path.split('/')[2].split('_')[1:]) # DATA/expname/data_corpus_speakingstyle/spk/*wav
+                    spk_match = audio_path.split('/')[3] # DATA/expname/data_corpus_speakingstyle/spk/*wav
                     if corpus == corpus_match and spk == spk_match: 
                         print('\nread wav-file {}'.format(audio_path))
                         freq, Nwav = calc_codebook_indexes(audio_path, freq, N)
-                        N = N + Nwav
-                        processed_files.append(audio_path)
+                        if Nwav != 0:
+                            N = N + Nwav
+                            processed_files.append(audio_path)
                 print(f'(DONE) Found {N} observations for speaker {spk} ...')
             # combine frequencies
             freqN_vec = np.zeros((1, 320**2), dtype=np.float32)

local/plot_pca_similarities.py

@@ -30,6 +30,7 @@ def main(X_path, y_path, pcaA_path, pcab_path, out_path, json_path):
     with open(json_path, 'r') as f:
         corpora = json.load(f)
 
+    smID = X_path.split('/')[-1].replace('.npy','')
     X = np.load(X_path)
     spks = np.load(y_path, allow_pickle=True)
 
@@ -52,7 +53,7 @@ def main(X_path, y_path, pcaA_path, pcab_path, out_path, json_path):
     plt.yticks(range(len(labels)), labels);
     cbar = fig.colorbar(cax, ticks=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, .8, .9, 1])
     cbar.ax.tick_params(labelsize=25)
-    fig_path = os.path.join(out_path,'{}.png'.format(X_path.split('/')[-1].replace('.npy','')))
+    fig_path = os.path.join(out_path,f'{smID}.png')
     print('\nsave similarity matrix to {}'.format(fig_path))
     plt.savefig(fig_path)
 
@@ -88,7 +89,7 @@ def main(X_path, y_path, pcaA_path, pcab_path, out_path, json_path):
     # Put a legend to the right of the current axis
     ax.legend(loc='center left', bbox_to_anchor=(1.175, .5))
     # Save the plot
-    fig_path = os.path.join(out_path,'scatter3D_{}.png'.format(X_path.split('/')[-1].replace('.npy','')))
+    fig_path = os.path.join(out_path,f'scatter_{smID}_PCA1_PCA2_PCA3.png')
     print('\nsave 3D scatter plot {}'.format(fig_path))
     plt.savefig(fig_path)
     
@@ -113,7 +114,8 @@ def main(X_path, y_path, pcaA_path, pcab_path, out_path, json_path):
         plt.ylabel(f'PCA{str(proj[1]+1)}')
         plt.grid()
         plt.tight_layout()
-        fig_path = os.path.join(out_path,'scatter_proj{}_{}.png'.format(''.join([str(proj[0]),str(proj[1])]), X_path.split('/')[-1].replace('.npy','')))
+        pcaID1, pcaID2 = str(proj[0]+1), str(proj[1]+1)
+        fig_path = os.path.join(out_path,f'scatter_{smID}_PCA{pcaID1}_PCA{pcaID2}.png')
         print('save 2D scatter plot {}'.format(fig_path))
         plt.savefig(fig_path)
 

run.sh

@@ -8,14 +8,15 @@ set -e -o pipefail
 . path.sh
 . conda.sh
 
-if [[ $# -eq 1 ]] ; then
-    echo 'ERROR: this run-script requires two arguments: DATA=? stage=?'
+if [[ $# -eq 2 ]] ; then
+    echo 'ERROR: this run-script requires two arguments: expdata=? expname=? stage=?'
     exit 1
 fi
 
-## runDATA/STAGE
-runDATA=$1
-stage=$2
+## expname/STAGE
+expdata=$1
+expname=$2
+stage=$3
 VERBOSE=1 # write logs for codebook frequency extraction?
 printf "\n### STAGE ###\n"
 printf "stage: %d\n" $stage
@@ -23,7 +24,7 @@ printf "### STAGE ###\n"
 
 ## DIRS/PATHS 
 model_path=model/xlsr_53_56k_new.pt
-exp_dir=exp_$runDATA
+exp_dir=exp_$expname
 
 ## STAGE 0: DELETE AND RUN ALL STAGES
 if [ $stage == 0 ]; then
@@ -34,7 +35,8 @@ fi
 ## print:
 printf "\nCWD: %s" "$CWD"
 printf "\nFAIRSEQ: %s" "$FAIRSEQ"
-printf "\nrunDATA: %s" "$runDATA"
+printf "\nexpname: %s" "$expname"
+printf "\nexpdata: %s" "$expdata"
 printf "\nmodel_path: %s" "$model_path"
 printf "\nexp_dir: %s\n\n" "$exp_dir"
 
@@ -50,22 +52,22 @@ mkdir -p $exp_dir/numpy/pca
 ## PREPARE DATA
 if [ $stage == 1 ]  || [ $stage == 0 ]; then
     printf "\n... Prepare data (*lst and *json) ...\n"
-    python3 local/prepare_data.py --output_lst_path $exp_dir/data/${runDATA}.lst \
-                                 --output_json_path $exp_dir/data/${runDATA}.json \
-                                 --DATA_dir ${runDATA}
+    python3 local/prepare_data.py --output_lst_path $exp_dir/data/${expname}.lst \
+                                 --output_json_path $exp_dir/data/${expname}.json \
+                                 --DATA_dir ${expdata}
 fi
 
 ## COUNT CODEBOOK USAGE
 if [ $stage == 2 ] || [ $stage == 0 ]; then
     printf "\n... Count frequencies of codebooks ...\n"
     python3 local/codebook_freqs.py $exp_dir \
-                                   $exp_dir/data/${runDATA}.lst \
-                                   $exp_dir/data/${runDATA}.json \
+                                   $exp_dir/data/${expname}.lst \
+                                   $exp_dir/data/${expname}.json \
                                    $model_path \
                                    $VERBOSE
     printf "\n... Combine Arrays ... \n"
     python3 local/codebook_combine_arrays.py $exp_dir/numpy \
-                                             $exp_dir/data/${runDATA}.json
+                                             $exp_dir/data/${expname}.json
 fi
 
 ## CALCULATE SIMILARITY MATRIX
@@ -75,7 +77,7 @@ if [ $stage == 3 ] || [ $stage == 0 ]; then
             $exp_dir/numpy/splits_freqs.npy \
             $exp_dir/numpy/splits_labels.npy \
             $exp_dir/numpy \
-            $exp_dir/data/${runDATA}.json
+            $exp_dir/data/${expname}.json
 fi
 
 ## PCA SPACE AND PLOTS
@@ -86,23 +88,26 @@ if [ $stage == 4 ] || [ $stage == 0 ]; then
         --output $exp_dir/numpy/pca \
         --dim 3
     printf "\n... PLOT similarity in PCA space (Analysis) ...\n"
+    mkdir -p $exp_dir/plots/analysis
     python3 local/plot_pca_similarities.py \
         $exp_dir/numpy/similarity_matrix.npy \
         $exp_dir/numpy/splits_labels.npy \
         $exp_dir/numpy/pca/3_pca_A.npy \
         $exp_dir/numpy/pca/3_pca_b.npy \
-        $exp_dir/plots \
-        $exp_dir/data/${runDATA}.json
+        $exp_dir/plots/analysis \
+        $exp_dir/data/${expname}.json
 fi
 
 if [ $stage == 5 ] || [ $stage == 0 ]; then
     printf "\n... Classification with k-means in PCA space ...\n"
+    mkdir -p $exp_dir/plots/kmeans
+    nclust=3 # number of clusters?
     python3 local/classification_kmeans.py \
         $exp_dir/numpy/similarity_matrix.npy \
         $exp_dir/numpy/splits_labels.npy \
         $exp_dir/numpy/pca/3_pca_A.npy \
         $exp_dir/numpy/pca/3_pca_b.npy \
-        $exp_dir/plots \
-        $exp_dir/data/${runDATA}.json \
-        2
+        $exp_dir/plots/kmeans \
+        $exp_dir/data/${expname}.json \
+        $nclust
 fi
\ No newline at end of file