simpler import with pandas.

Author: vincentqb

examples/pipeline_wav2letter/README.md

@@ -2,7 +2,7 @@ This is an example pipeline for speech recognition using a greedy or Viterbi CTC
 
 ### Usage
 
-More information about each command line parameters is available with the `--help` option. An example reproducing the demo can be invoked as follows.
+More information about each command line parameters is available with the `--help` option. An example can be invoked as follows.
 ```
 python main.py \
     --reduce-lr-valid \
@@ -28,30 +28,12 @@ With these default parameters, we get a character error rate of 13.8% on dev-cle
 ### Output
 
 The information reported at each iteration and epoch (e.g. loss, character error rate, word error rate) is printed to standard output in the form of one json per line, e.g.
-```
+```python
 {"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 23317.0, "total chars": 23317.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 4446.0, "total words": 4446.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 2453, "dataset length": 128.0, "iteration": 1.0, "loss": 8.712121963500977, "cumulative loss": 8.712121963500977, "average loss": 8.712121963500977, "iteration time": 41.46276903152466, "epoch time": 41.46276903152466}
 {"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 46005.0, "total chars": 46005.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 8762.0, "total words": 8762.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 1703, "dataset length": 256.0, "iteration": 2.0, "loss": 8.918599128723145, "cumulative loss": 17.63072109222412, "average loss": 8.81536054611206, "iteration time": 1.2905676364898682, "epoch time": 42.753336668014526}
 {"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 70030.0, "total chars": 70030.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 13348.0, "total words": 13348.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 1713, "dataset length": 384.0, "iteration": 3.0, "loss": 8.550191879272461, "cumulative loss": 26.180912971496582, "average loss": 8.726970990498861, "iteration time": 1.2109291553497314, "epoch time": 43.96426582336426}
 ```
-Further information is reported to standard error. Here is an example python function to parse the standard output when saved to a file.
-```python
-def read_json(filename):
-	"""
-	Convert the standard output saved to filename into a pandas dataframe for analysis.
-	"""
-
-	import pandas
-	import json
-
-    with open(filename, "r") as f:
-        data = f.read()
-
-    # pandas doesn't read single quotes for json
-    data = data.replace("'", '"')
-
-    data = [json.loads(l) for l in data.splitlines()]
-    return pandas.DataFrame(data)
-```
+One way to import the output in python with pandas is by saving the standard output to a file, and then using `pandas.read_json(filename, lines=True)`.
 
 ## Structure of pipeline