From 179ba1b5339b24087065ae0d344208d3542c2f0a Mon Sep 17 00:00:00 2001
From: Quentin Brunet <hello@quentinbrunet.com>
Date: Sun, 3 Mar 2019 17:27:50 +0100
Subject: [PATCH] Fix import_cv2.py binary file + permissions

---
 bin/import_cv2.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)
 mode change 100644 => 100755 bin/import_cv2.py

diff --git a/bin/import_cv2.py b/bin/import_cv2.py
old mode 100644
new mode 100755
index c20b0069..d8f94bf3
--- a/bin/import_cv2.py
+++ b/bin/import_cv2.py
@@ -20,16 +20,16 @@ from util.downloader import SIMPLE_BAR
 
 '''
 Broadly speaking, this script takes the audio downloaded from Common Voice
-for a certain language, in addition to the *.tsv files output by CorporaCeator,
+for a certain language, in addition to the *.tsv files output by CorporaCreator,
 and the script formats the data and transcripts to be in a state usable by
 DeepSpeech.py
 
 Usage:
         $ python3 import_cv2.py /path/to/audio/data_dir /path/to/tsv_dir
 
-Input: 
+Input:
         (1) audio_dir (string) path to dir of audio downloaded from Common Voice
-        (2) tsv_dir (string) path to dir containing {train,test,dev}.tsv files 
+        (2) tsv_dir (string) path to dir containing {train,test,dev}.tsv files
             which were generated by CorporaCreator
 
 Ouput:
@@ -53,23 +53,25 @@ def _preprocess_data(audio_dir, tsv_dir):
 def _maybe_convert_set(audio_dir, input_tsv):
     output_csv =  path.join(audio_dir,os.path.split(input_tsv)[-1].replace('tsv', '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) as input_tsv_file:
         reader = csv.DictReader(input_tsv_file, delimiter='\t')
         for row in reader:
             samples.append((row['path'], row['sentence']))
-            
+
     # Keep track of how many samples are good vs. problematic
     counter = { 'all': 0, 'too_short': 0, 'too_long': 0 }
     lock = RLock()
     num_samples = len(samples)
     rows = []
-    
+
     def one_sample(sample):
         """ Take a audio file, and optionally convert it to 16kHz WAV """
         mp3_filename = path.join(audio_dir, sample[0])
+        if not path.splitext(mp3_filename.lower())[1] == '.mp3':
+            mp3_filename += ".mp3"
         # Storing wav files next to the mp3 ones - just with a different suffix
         wav_filename = path.splitext(mp3_filename)[0] + ".wav"
         _maybe_convert_wav(mp3_filename, wav_filename)
@@ -86,7 +88,7 @@ def _maybe_convert_set(audio_dir, input_tsv):
                 # This one is good - keep it for the target CSV
                 rows.append((wav_filename, file_size, sample[1]))
             counter['all'] += 1
-            
+
     print("Importing mp3 files...")
     pool = Pool(cpu_count())
     bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
@@ -95,7 +97,7 @@ def _maybe_convert_set(audio_dir, input_tsv):
     bar.update(num_samples)
     pool.close()
     pool.join()
-    
+
     with open(output_csv, 'w') as output_csv_file:
         print('Writing CSV file for DeepSpeech.py as: ', output_csv)
         writer = csv.DictWriter(output_csv_file, fieldnames=FIELDNAMES)
@@ -103,7 +105,7 @@ def _maybe_convert_set(audio_dir, input_tsv):
         bar = progressbar.ProgressBar(max_value=len(rows), widgets=SIMPLE_BAR)
         for filename, file_size, transcript in bar(rows):
             writer.writerow({ 'wav_filename': filename, 'wav_filesize': file_size, 'transcript': transcript })
-            
+
     print('Imported %d samples.' % (counter['all'] - counter['too_short'] - counter['too_long']))
     if counter['too_short'] > 0:
         print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])