Trained model on my own data

DeepSpeech
#30

I have changed script as you said for 44100 hz and stereo channel
changed

this is the log what I got

Help me remove that error

Epoch 0 | Training | Elapsed Time: 1:52:06 | Steps: 96 | Loss: 531.260859
Epoch 0 | Validation | Elapsed Time: 0:01:07 | Steps: 12 | Loss: 819.303584 | Dataset: /app/Deepspeech/dev/dev.csv
I Saved new best validating model with loss 819.303584 to: /app/Deepspeech/results/checkout/best_dev-1836
Epoch 1 | Training | Elapsed Time: 1:41:58 | Steps: 96 | Loss: 506.411064
Epoch 1 | Validation | Elapsed Time: 0:01:10 | Steps: 12 | Loss: 793.307281 | Dataset: /app/Deepspeech/dev/dev.csv
I Saved new best validating model with loss 793.307281 to: /app/Deepspeech/results/checkout/best_dev-1932
Epoch 2 | Training | Elapsed Time: 1:35:40 | Steps: 96 | Loss: 476.467811
Epoch 2 | Validation | Elapsed Time: 0:01:06 | Steps: 12 | Loss: 793.474063 | Dataset: /app/Deepspeech/dev/dev.csv
Epoch 3 | Training | Elapsed Time: 1:30:58 | Steps: 96 | Loss: 430.477815
Epoch 3 | Validation | Elapsed Time: 0:01:04 | Steps: 12 | Loss: 739.606102 | Dataset: /app/Deepspeech/dev/dev.csv
I Saved new best validating model with loss 739.606102 to: /app/Deepspeech/results/checkout/best_dev-2124
Epoch 4 | Training | Elapsed Time: 1:26:48 | Steps: 96 | Loss: 390.402085
Epoch 4 | Validation | Elapsed Time: 0:01:07 | Steps: 12 | Loss: 811.324318 | Dataset: /app/Deepspeech/dev/dev.csv
I Early stop triggered as (for last 4 steps) validation loss: 811.324318 with standard deviation: 25.354381 and mean: 775.462482
I FINISHED optimization in 8:13:11.060505
I Restored variables from best validation checkpoint at /app/Deepspeech/results/checkout/best_dev-2124, step 2124
Testing model on /app/Deepspeech/test/test.csv
Test epoch | Steps: 25 | Elapsed Time: 0:01:12
Test on /app/Deepspeech/test/test.csv - WER: 0.992800, CER: 0.986946, loss: 800.126892

WER: 1.000000, CER: 0.985000, loss: 29.460112

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WER: 1.000000, CER: 0.985000, loss: 34.642292

  • src: "one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one "
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WER: 1.000000, CER: 0.985000, loss: 43.823837

  • src: "one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one "
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WER: 1.000000, CER: 0.988000, loss: 829.879456

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WER: 1.000000, CER: 0.988000, loss: 830.506531

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WER: 1.000000, CER: 0.988000, loss: 831.406311

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WER: 1.000000, CER: 0.988000, loss: 833.888916

  • src: "five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five five "
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WER: 1.000000, CER: 0.988000, loss: 834.442749

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WER: 1.000000, CER: 0.988000, loss: 843.434326

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WER: 1.000000, CER: 0.988000, loss: 846.850525

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WER: 1.000000, CER: 0.992000, loss: 906.634338

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WER: 1.000000, CER: 0.992000, loss: 917.082153

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WER: 1.000000, CER: 0.992000, loss: 931.485535

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WER: 1.000000, CER: 0.992000, loss: 971.765442

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WER: 1.000000, CER: 0.992000, loss: 980.901733

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WER: 1.000000, CER: 0.996000, loss: 985.703308

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WER: 1.000000, CER: 0.992000, loss: 1011.776367

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WER: 1.000000, CER: 0.996000, loss: 1016.469482

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WER: 1.000000, CER: 0.996000, loss: 1033.371948

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WER: 1.000000, CER: 0.986667, loss: 1068.499268

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WER: 1.000000, CER: 0.986667, loss: 1087.635254

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WER: 1.000000, CER: 0.986667, loss: 1108.907349

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WER: 1.000000, CER: 0.986667, loss: 1111.774170

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WER: 1.000000, CER: 0.986667, loss: 1114.400024

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WER: 1.000000, CER: 0.986667, loss: 1125.432251

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WER: 1.000000, CER: 0.986667, loss: 1149.028564

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WER: 1.000000, CER: 0.986667, loss: 1159.635742

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WER: 1.000000, CER: 0.986667, loss: 1185.972534

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WER: 0.980000, CER: 0.983333, loss: 569.788208

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WER: 0.980000, CER: 0.984000, loss: 654.026611

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WER: 0.980000, CER: 0.985000, loss: 656.827332

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WER: 0.980000, CER: 0.985000, loss: 659.727051

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WER: 0.980000, CER: 0.984000, loss: 661.934265

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WER: 0.980000, CER: 0.985000, loss: 666.090271

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WER: 0.980000, CER: 0.984000, loss: 671.630920

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WER: 0.980000, CER: 0.985000, loss: 672.173218

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WER: 0.980000, CER: 0.985000, loss: 673.630249

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WER: 0.980000, CER: 0.984000, loss: 675.070740

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WER: 0.980000, CER: 0.984000, loss: 682.800720

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WER: 0.980000, CER: 0.984000, loss: 692.452820

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WER: 0.980000, CER: 0.984000, loss: 695.382751

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WER: 0.980000, CER: 0.985000, loss: 696.252136

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WER: 0.980000, CER: 0.984000, loss: 713.148743

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WER: 0.980000, CER: 0.984000, loss: 775.913818

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WER: 0.980000, CER: 0.984000, loss: 822.966125

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WER: 0.980000, CER: 0.983333, loss: 935.471558

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WER: 0.980000, CER: 0.983333, loss: 966.865479

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WER: 0.980000, CER: 0.983333, loss: 988.343689

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WER: 0.980000, CER: 0.983333, loss: 1004.328613

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WER: 0.980000, CER: 0.983333, loss: 1031.770996

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WER: 0.980000, CER: 0.983333, loss: 1040.531250

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WER: 0.980000, CER: 0.983333, loss: 1045.897217

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WER: 0.980000, CER: 0.983333, loss: 1050.801758

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WER: 0.980000, CER: 0.983333, loss: 1061.078735

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WER: 0.980000, CER: 0.983333, loss: 1063.963989

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WER: 0.980000, CER: 0.983333, loss: 1065.088745

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WER: 0.980000, CER: 0.983333, loss: 1067.910645

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WER: 0.980000, CER: 0.983333, loss: 1143.155151

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WER: 0.980000, CER: 0.983333, loss: 1165.391235

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WER: 0.980000, CER: 0.983333, loss: 1183.412720

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WER: 0.980000, CER: 0.983333, loss: 1239.415771

  • src: "three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three "
  • res: “three”

WER: 0.980000, CER: 0.983333, loss: 1263.958374

  • src: "three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three "
  • res: “three”

WER: 0.980000, CER: 0.983333, loss: 1275.644775

  • src: "three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three "
  • res: “three”

WER: 0.980000, CER: 0.983333, loss: 1326.395996

  • src: "three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three "
  • res: “three”

I Exporting the model…
Traceback (most recent call last):
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/execute.py”, line 145, in make_shape
shape = tensor_shape.as_shape(v)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 1125, in as_shape
return TensorShape(shape)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 690, in init
self._dims = [as_dimension(d) for d in dims_iter]
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 690, in
self._dims = [as_dimension(d) for d in dims_iter]
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 632, in as_dimension
return Dimension(value)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 188, in init
raise ValueError(“Ambiguous dimension: %s” % value)
ValueError: Ambiguous dimension: 1411.2

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File “DeepSpeech.py”, line 836, in
tf.app.run(main)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/platform/app.py”, line 125, in run
_sys.exit(main(argv))
File “DeepSpeech.py”, line 828, in main
export()
File “DeepSpeech.py”, line 687, in export
inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=FLAGS.n_steps, tflite=FLAGS.export_tflite)
File “DeepSpeech.py”, line 568, in create_inference_graph
input_samples = tf.placeholder(tf.float32, [Config.audio_window_samples], ‘input_samples’)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py”, line 2077, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py”, line 5789, in placeholder
shape = _execute.make_shape(shape, “shape”)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/execute.py”, line 150, in make_shape
e))
ValueError: Error converting shape to a TensorShape: Ambiguous dimension: 1411.2.

#31

@lucifera678 Can you use proper code formatting for console / log output ? It’s complicated to read otherwise.

#32

Again, can you share your changes ?

#33

(.virtualenv) kdavis-19htdh:DeepSpeech kdavis$ find . -name “*.py” -exec grep 16000 {} /dev/null ;
./util/flags.py: f.DEFINE_integer(‘audio_sample_rate’, 16000, ‘sample rate value expected by model’)
./bin/import_cv2.py:SAMPLE_RATE = 16000
./bin/import_fisher.py: origAudios = [librosa.load(wav_file, sr=16000, mono=False) for wav_file in wav_files]
./bin/import_swb.py: audioData, frameRate = librosa.load(temp_wav_file, sr=16000, mono=True)
./bin/import_ts.py:SAMPLE_RATE = 16000
./bin/import_cv.py:SAMPLE_RATE = 16000
./bin/import_gram_vaani.py:SAMPLE_RATE = 16000
./bin/import_lingua_libre.py:SAMPLE_RATE = 16000
./bin/import_aishell.py: durations = (df[‘wav_filesize’] - 44) / 16000 / 2
./examples/vad_transcriber/wavTranscriber.py: audio_length = len(audio) * (1 / 16000)
./examples/vad_transcriber/wavTranscriber.py: assert sample_rate == 16000, “Only 16000Hz input WAV files are supported for now!”
./examples/vad_transcriber/wavSplit.py: assert sample_rate in (8000, 16000, 32000)
./examples/mic_vad_streaming/mic_vad_streaming.py: RATE_PROCESS = 16000
./examples/mic_vad_streaming/mic_vad_streaming.py: “”“Return a block of audio data resampled to 16000hz, blocking if necessary.”""
./examples/mic_vad_streaming/mic_vad_streaming.py: DEFAULT_SAMPLE_RATE = 16000
./stats.py: parser.add_argument("–sample-rate", type=int, default=16000, required=False, help=“Audio sample rate”)
./native_client/python/client.py: sox_cmd = 'sox {} --type raw --bits 16 --channels 1 --rate 16000 --encoding signed-integer --endian little --compression 0.0 --no-dither - '.format(quote(audio_path))
./native_client/python/client.py: return 16000, np.frombuffer(output, np.int16)
./native_client/python/client.py: if fs != 16000:
./native_client/python/client.py: audio_length = fin.getnframes() * (1/16000)
./native_client/python/init.py: def setupStream(self, pre_alloc_frames=150, sample_rate=16000):

changed in all this files as you mentioned above

#34

Please, this is absolutely not useful. Can’t you git diff and share the changes appropriately using code formatting ?

#35

@lucifera678,
40s sequences to train are very large !!
You took a lot of time to train your model, however you obtain a wer. 99!!
99% error !!
It’s normal that the results are poor…

Sure that you should try training with only 10 sentences, 16k mono, max 15s… See the results…correct the params… Anderstand…

And restart later with all your datas.
Good luck
Vincent

#36

WER: 0.980000, CER: 0.983333, loss: 1326.395996
• src: "three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three three "
• res: “three”

I Exporting the model…
Traceback (most recent call last):
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/execute.py”, line 145, in make_shape
shape = tensor_shape.as_shape(v)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 1125, in as_shape
return TensorShape(shape)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 690, in init
self._dims = [as_dimension(d) for d in dims_iter]
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 690, in
self._dims = [as_dimension(d) for d in dims_iter]
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 632, in as_dimension
return Dimension(value)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py”, line 188, in init
raise ValueError(“Ambiguous dimension: %s” % value)
ValueError: Ambiguous dimension: 1411.2
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File “DeepSpeech.py”, line 836, in
tf.app.run(main)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/platform/app.py”, line 125, in run
_sys.exit(main(argv))
File “DeepSpeech.py”, line 828, in main
export()
File “DeepSpeech.py”, line 687, in export
inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=FLAGS.n_steps, tflite=FLAGS.export_tflite)
File “DeepSpeech.py”, line 568, in create_inference_graph
input_samples = tf.placeholder(tf.float32, [Config.audio_window_samples], ‘input_samples’)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py”, line 2077, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py”, line 5789, in placeholder
shape = _execute.make_shape(shape, “shape”)
File “/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/execute.py”, line 150, in make_shape
e))
ValueError: Error converting shape to a TensorShape: Ambiguous dimension: 1411.2.

I have formated the code is there anything that can I do

#37

Sharing the diff of your change s?

#38

./util/flags.py: f.DEFINE_integer(‘audio_sample_rate’, 44100, ‘sample rate value expected by model’)
./bin/import_cv2.py:SAMPLE_RATE = 44100
./bin/import_fisher.py: origAudios = [librosa.load(wav_file, sr= 44100, mono=False) for wav_file in wav_files]
./bin/import_swb.py: audioData, frameRate = librosa.load(temp_wav_file, sr= 44100, mono=True)
./bin/import_ts.py:SAMPLE_RATE = 44100
./bin/import_cv.py:SAMPLE_RATE = 44100
./bin/import_gram_vaani.py:SAMPLE_RATE = 44100
./bin/import_lingua_libre.py:SAMPLE_RATE = 44100
./bin/import_aishell.py: durations = (df[‘wav_filesize’] - 44) / 44100 / 2
./examples/vad_transcriber/wavTranscriber.py: audio_length = len(audio) * (1 / 44100)
./examples/vad_transcriber/wavTranscriber.py: assert sample_rate == 16000, “Only 16000Hz input WAV files are supported for now!”
./examples/vad_transcriber/wavSplit.py: assert sample_rate in (8000, 16000, 32000, 44100)
./examples/mic_vad_streaming/mic_vad_streaming.py: RATE_PROCESS = 44100
./examples/mic_vad_streaming/mic_vad_streaming.py: “”“Return a block of audio data resampled to 16000hz, blocking if necessary.”""
./examples/mic_vad_streaming/mic_vad_streaming.py: DEFAULT_SAMPLE_RATE = 44100
./stats.py: parser.add_argument("–sample-rate", type=int, default= 44100, required=False, help=“Audio sample rate”)
./native_client/python/client.py: sox_cmd = 'sox {} --type raw --bits 16 --channels 1 --rate 44100 --encoding signed-integer --endian little --compression 0.0 --no-dither - '.format(quote(audio_path))
./native_client/python/client.py: return 44100, np.frombuffer(output, np.int16)
./native_client/python/client.py: if fs != 44100:
./native_client/python/client.py: audio_length = fin.getnframes() * (1/ 44100)
./native_client/python/ init .py: def setupStream(self, pre_alloc_frames=150, sample_rate= 44100):

All the Bold formatted text are the changes in the files as you said me to change 16000 to 44100

#39

I’m sorry, that’s still not a diff as I asked. It’s completely unusable.

#40

@lucifera678 maybe you aren’t aware what a diff is? If not, one of these might give you a bit of background:

https://www.git-tower.com/learn/git/ebook/en/command-line/advanced-topics/diffs

#41

Could you upload your data somewhere? I could try to train a model and give you the config you need. Feels like that’s easier than debugging such specific issues.

#42

@lucifera678
ok ok listen here. go to util/flags.py . change audio_sample_rate to 16000(you set it as 44100). and then you’ll see that you can export your model.

Do i know if that screws up your model? I do not. But can you export it? yes. good luck.

Resample training input samples to align with inference restraints
#43

Is there restriction for wav file length for training?

#44

You want to use data with max length 15 seconds, optimally even less than that (Mozilla model is trained on files with max length 8 seconds if I recall correctly).

More on how to deal with it here: Longer audio files with Deep Speech
Moreover in DeepSpeech source code in examples there are some scripts where vad transcriber was presented.

#45

(venv) sehar@sehar-HP-Z220-CMT-Workstation:~/DeepSpeech$ python speech.py /home/sehar/urdu-models/output_graph1.pb /home/sehar/urdu-models/alphabet1.txt /home/sehar/urdu-models/sent6urd.wav
TensorFlow: v1.13.1-10-g3e0cc53
DeepSpeech: v0.5.1-0-g4b29b78
Warning: reading entire model file into memory. Transform model file into an mmapped graph to reduce heap usage.
2019-11-26 11:35:11.022262: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-26 11:35:11.023504: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.759
pciBusID: 0000:01:00.0
totalMemory: 5.93GiB freeMemory: 5.59GiB
2019-11-26 11:35:11.023541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-11-26 11:35:15.973514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-26 11:35:15.973566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-11-26 11:35:15.973593: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-11-26 11:35:16.001763: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5371 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-11-26 11:35:17.972698: E tensorflow/core/framework/op_kernel.cc:1325] OpKernel (‘op: “UnwrapDatasetVariant” device_type: “CPU”’) for unknown op: UnwrapDatasetVariant
2019-11-26 11:35:17.972759: E tensorflow/core/framework/op_kernel.cc:1325] OpKernel (‘op: “WrapDatasetVariant” device_type: “GPU” host_memory_arg: “input_handle” host_memory_arg: “output_handle”’) for unknown op: WrapDatasetVariant
2019-11-26 11:35:17.972786: E tensorflow/core/framework/op_kernel.cc:1325] OpKernel (‘op: “WrapDatasetVariant” device_type: “CPU”’) for unknown op: WrapDatasetVariant
2019-11-26 11:35:17.972954: E tensorflow/core/framework/op_kernel.cc:1325] OpKernel (‘op: “UnwrapDatasetVariant” device_type: “GPU” host_memory_arg: “input_handle” host_memory_arg: “output_handle”’) for unknown op: UnwrapDatasetVariant
Error running session: Not found: PruneForTargets: Some target nodes not found: initialize_state
Segmentation fault (core dumped)
after training my model i tested it and its giving me this error

#46

@sehar_capricon Please please please, can you really make an effort and USE CODE FORMATTING ? Your output is hard to read, this is DIFFICULT for people to help you.

You are running binary v0.5.1, your error would suggest you trained from current master which targets v0.6.x binaries.

#47

thanks for your prompt response,
i have trained my model on deepspeech v 0.5.1

#48

Can you triple check that ? How did you performed the export ?

#49

you can improve the accuracy of your model by changing the audio file length to be between 15 to 20 seconds