diff --git a/bin/import_cv2.py b/bin/import_cv2.py
index df098f5c..7c85c8f0 100755
--- a/bin/import_cv2.py
+++ b/bin/import_cv2.py
@@ -31,11 +31,14 @@ MAX_SECS = 10
 
 
 def _preprocess_data(tsv_dir, audio_dir, space_after_every_character=False):
-    for dataset in ["train", "test", "dev", "validated", "other"]:
-        input_tsv = os.path.join(os.path.abspath(tsv_dir), dataset + ".tsv")
-        if os.path.isfile(input_tsv):
-            print("Loading TSV file: ", input_tsv)
-            _maybe_convert_set(input_tsv, audio_dir, space_after_every_character)
+    exclude = []
+    for dataset in ["test", "dev", "train", "validated", "other"]:
+        set_samples = _maybe_convert_set(dataset, tsv_dir, audio_dir, space_after_every_character)
+        if dataset in ["test", "dev"]:
+            exclude += set_samples
+        if dataset == "validated":
+            _maybe_convert_set("train-all", tsv_dir, audio_dir, space_after_every_character,
+                               rows=set_samples, exclude=exclude)
 
 
 def one_sample(sample):
@@ -72,47 +75,63 @@ def one_sample(sample):
         counter["too_long"] += 1
     else:
         # This one is good - keep it for the target CSV
-        rows.append((os.path.split(wav_filename)[-1], file_size, label))
+        rows.append((os.path.split(wav_filename)[-1], file_size, label, sample[2]))
     counter["all"] += 1
     counter["total_time"] += frames
 
     return (counter, rows)
 
 
-def _maybe_convert_set(input_tsv, audio_dir, space_after_every_character=None):
-    output_csv = os.path.join(
-        audio_dir, os.path.split(input_tsv)[-1].replace("tsv", "csv")
-    )
+def _maybe_convert_set(dataset, tsv_dir, audio_dir, space_after_every_character=None, rows=None, exclude=None):
+    exclude_transcripts = set()
+    exclude_speakers = set()
+    if exclude is not None:
+        for sample in exclude:
+            exclude_transcripts.add(sample[2])
+            exclude_speakers.add(sample[3])
+
+    if rows is None:
+        rows = []
+        input_tsv = os.path.join(os.path.abspath(tsv_dir), dataset + ".tsv")
+        if not os.path.isfile(input_tsv):
+            return rows
+        print("Loading TSV file: ", input_tsv)
+        # Get audiofile path and transcript for each sentence in tsv
+        samples = []
+        with open(input_tsv, encoding="utf-8") as input_tsv_file:
+            reader = csv.DictReader(input_tsv_file, delimiter="\t")
+            for row in reader:
+                samples.append((os.path.join(audio_dir, row["path"]), row["sentence"], row["client_id"]))
+
+        counter = get_counter()
+        num_samples = len(samples)
+
+        print("Importing mp3 files...")
+        pool = Pool()
+        bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
+        for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
+            counter += processed[0]
+            rows += processed[1]
+            bar.update(i)
+        bar.update(num_samples)
+        pool.close()
+        pool.join()
+
+        imported_samples = get_imported_samples(counter)
+        assert counter["all"] == num_samples
+        assert len(rows) == imported_samples
+        print_import_report(counter, SAMPLE_RATE, MAX_SECS)
+
+    output_csv = os.path.join(os.path.abspath(audio_dir), dataset + ".csv")
     print("Saving new DeepSpeech-formatted CSV file to: ", output_csv)
-
-    # Get audiofile path and transcript for each sentence in tsv
-    samples = []
-    with open(input_tsv, encoding="utf-8") as input_tsv_file:
-        reader = csv.DictReader(input_tsv_file, delimiter="\t")
-        for row in reader:
-            samples.append((os.path.join(audio_dir, row["path"]), row["sentence"]))
-
-    counter = get_counter()
-    num_samples = len(samples)
-    rows = []
-
-    print("Importing mp3 files...")
-    pool = Pool()
-    bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
-    for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
-        counter += processed[0]
-        rows += processed[1]
-        bar.update(i)
-    bar.update(num_samples)
-    pool.close()
-    pool.join()
-
     with open(output_csv, "w", encoding="utf-8") as output_csv_file:
         print("Writing CSV file for DeepSpeech.py as: ", output_csv)
         writer = csv.DictWriter(output_csv_file, fieldnames=FIELDNAMES)
         writer.writeheader()
         bar = progressbar.ProgressBar(max_value=len(rows), widgets=SIMPLE_BAR)
-        for filename, file_size, transcript in bar(rows):
+        for filename, file_size, transcript, speaker in bar(rows):
+            if transcript in exclude_transcripts or speaker in exclude_speakers:
+                continue
             if space_after_every_character:
                 writer.writerow(
                     {
@@ -129,12 +148,7 @@ def _maybe_convert_set(input_tsv, audio_dir, space_after_every_character=None):
                         "transcript": transcript,
                     }
                 )
-
-    imported_samples = get_imported_samples(counter)
-    assert counter["all"] == num_samples
-    assert len(rows) == imported_samples
-
-    print_import_report(counter, SAMPLE_RATE, MAX_SECS)
+    return rows
 
 
 def _maybe_convert_wav(mp3_filename, wav_filename):