Updated readme to give info on merging datasets

README.md

@@ -3,6 +3,7 @@ language:
 - tr
 datasets:
 - common_voice 
+- movies
 metrics:
 - wer
 tags:
@@ -12,7 +13,7 @@ tags:
 - xlsr-fine-tuning-week
 license: apache-2.0
 model-index:
-- name: XLSR Wav2Vec2 Large Turkish by Gorkem Goknar
+- name: XLSR Wav2Vec2 Large Turkish by Gorkem Goknar 
   results:
   - task: 
       name: Speech Recognition
@@ -24,18 +25,17 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: TBD
+         value: 50.41
 ---
 # Wav2Vec2-Large-XLSR-53-Turkish
 
-Note: Common voice Turkish data is no background noise voice only, slower than usual day speech dataset.
-In this model although Word Error rate for test is 50% it is agains Common Voice text.
+Note: This model is trained with 5 Turkish movies additional to common voice dataset.
+Although WER is high (50%) per common voice test dataset, its recognition (with some letter errors) seems better.
 
-Please try speech yourself and see it is converting pretty good .
-I hope some news channels or movie producers lets use their data for test/training (I asked some no reply)
+Please try speech yourself on the right side to see its performance.
 
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice) and 5 Turkish movies that include background noise/talkers .
 
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice).
 When using this model, make sure that your speech input is sampled at 16kHz.
 ## Usage
 The model can be used directly (without a language model) as follows:
@@ -132,11 +132,10 @@ def audio_resampler(batch, new_sample_rate = 16000):
     #not working without complex library compilation in windows for mp3
     #speech_array, sampling_rate = torchaudio.load(batch["path"])
     #speech_array, sampling_rate = librosa.load(batch["path"])
-
     #sampling_rate =  pydub.utils.info['sample_rate']  ##gets current samplerate
     
     sound = pydub.AudioSegment.from_file(file=batch["path"])
-    sampling_rate = new_sample_rate
+
     sound = sound.set_frame_rate(new_sample_rate)
     left = sound.split_to_mono()[0]
     bit_depth = left.sample_width * 8
@@ -146,23 +145,17 @@ def audio_resampler(batch, new_sample_rate = 16000):
 
     speech_array = torch.FloatTensor(numeric_array)
     
-    batch["speech"] = numeric_array
-    batch["sampling_rate"] = sampling_rate
-    #batch["target_text"] = batch["sentence"]
-
-    return batch
+    
+    return speech_array, new_sample_rate
 
 def remove_special_characters(batch):
 
     ##this one comes from subtitles if additional timestamps not processed  -> 00:01:01   00:01:01,33
     batch["sentence"] = re.sub('\b\d{2}:\d{2}:\d{2}(,+\d{2})?\b', ' ', batch["sentence"])
-
     ##remove all caps in text [AÇIKLAMA] etc, do it before..
     batch["sentence"] = re.sub('\[(\b[A-Z]+\])', '', batch["sentence"]) 
-
     ##replace three dots (that are inside string with single)
     batch["sentence"] = re.sub("([a-zA-Z]+)\.\.\.", r"\1.", batch["sentence"])
-
     #standart ignore list
     batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
     
@@ -171,12 +164,18 @@ def remove_special_characters(batch):
     
 # Preprocessing the datasets.
 # We need to read the aduio files as arrays
+new_sample_rate = 16000
 def speech_file_to_array_fn(batch):
     batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() 
     ##speech_array, sampling_rate = torchaudio.load(batch["path"])
     ##load and conversion done in resampler , takes and returns batch
-    batch = audio_resampler(batch, new_sample_rate = new_sample_rate)
+    speech_array, sampling_rate = audio_resampler(batch, new_sample_rate = new_sample_rate)
+    batch["speech"] = speech_array
+    batch["sampling_rate"] = sampling_rate
+    batch["target_text"] = batch["sentence"]
+
     return batch
+
 test_dataset = test_dataset.map(speech_file_to_array_fn)
 # Preprocessing the datasets.
 # We need to read the aduio files as arrays
@@ -196,7 +195,64 @@ result = test_dataset.map(evaluate, batched=True, batch_size=2)
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
 
 ```
+
 **Test Result**: 50.41 %  
+
+
 ## Training
-The Common Voice `train` and `validation` datasets were used for training. Additional 5 Turkish movies with subtitles also used.
-Training still continues...
+
+
+The Common Voice `train` and `validation` datasets were used for training. Additional 5 Turkish movies with subtitles also used for training.
+Similar training model used as base fine-tuning, additional audio resampler is on above code.
+
+Putting model building and merging code below for reference
+
+
+```python
+import pandas as pd 
+from datasets import load_dataset, load_metric
+
+import os 
+from pathlib import Path
+from datasets import Dataset
+import csv
+
+#Walk all subdirectories of base_set_path and find csv files
+base_set_path = r"C:\dataset_extracts\"
+
+csv_files = []
+for path, subdirs, files in os.walk(base_set_path):
+    for name in files:
+        if name.endswith(".csv"):
+            deckfile= os.path.join(path, name)
+            csv_files.append(deckfile)
+
+def get_dataset_from_csv_file(csvfilename,names=['sentence', 'path']):
+  path = Path(csvfilename)
+  csv_delimiter="\t"  ##tab seperated, change if something else
+  
+  ##Pandas has bug reading non-ascii file names, make sure use open with encoding
+  df=pd.read_csv(open(path, 'r', encoding='utf-8'), delimiter=csv_delimiter,header=None , names=names, encoding='utf8')
+  return Dataset.from_pandas(df)
+
+custom_datasets= []
+for csv_file in csv_files:
+  this_dataset=get_dataset_from_csv_file(csv_file) 
+  custom_datasets.append(this_dataset)
+
+
+
+
+from datasets import concatenate_datasets, load_dataset
+from datasets import load_from_disk
+
+# Merge datasets together (from csv files)
+dataset_file_path = ".\dataset_file"
+custom_datasets_concat = concatenate_datasets( [dset for dset in custom_datasets] )
+
+#save this one to disk
+custom_datasets_concat.save_to_disk( dataset_file_path ) 
+
+#load back from disk
+custom_datasets_from_disk = load_from_disk(dataset_file_path)
+```