🌟 Weights & Biases - Supporting Wave2Vec2 Finetuning!

[morgan]

Hey folks!

First off huge thanks to @patrickvonplaten and @valhalla for putting this challenge together, really excited to do some Irish language training!

I’m also really happy to say that the team here at Weights & Biases would like to support the community with their training as much as we can!

(If you haven’t used Weights & Biases before you can find brand new docs for our Hugging Face integration here or check out the XLSR colab linked below)

Quick Summary

• Weights & Biases will create public language-specific W&B Projects so that multiple people can collaborate effectively on the same language, add your language here
• We have a beta dataset visualisation feature that I think is suuper useful to explore speech datasets
• We have a W&B XLSR Colab to show off how best to instrument your code to log your models training progress as well as upload and version your datasets, tokenizers, processors and models (before logging your best model to the HF Model Hub ).

Language-specific W&B Projects - just ask

In order to help organise multiple people working on the same language, we are happy to create public language-specific W&B Projects that anyone will be able to log their results, datasets, models, tokenizers etc to. This way folks working on the same language can work as a team and can easily share results and see the configs and hyperparameters that were used for specific model runs.

Go here to add the language-specific project you’d like us to request

W&B Dataset Visualisation [Beta]

I’m suuuper excited about using this feature to quickly explore speech datasets

With this new W&B feature (still in Beta) you can easily explore rich media tables to better understand your speech dataset. I’ve made a quick video demo which I think will best explain the value of this feature for EDA of rich media such as audio and video.

To see the code that created this rich media table in W&B Artifacts, see the W&B XLSR Colab

This is still in beta and the team would love to hear any feedback you have on it, please feel free to ping me about it and I can pass it on to the team Docs are here for more info.

W&B XLSR Colab

We have also created a W&B XLSR Colab with setup and training from top to bottom to show how you can get the most out of Weights & Biases. Get wandb setup to log your models training as well as version your datasets, tokenizers, processors and models!

To make finding relevant wandb code a bit easier, the relevant headings in the notebook start with “WANDB: …” Just search “wandb” to jump through and find the wandb code you’re looking for!

Let us know how it goes!

Let use know how integrating and using W&B goes and whether you have any issues! I’ll be active here and in the Hugging Face XLSR slack channel to answer any Weights & Biases questions you might have! @boris will also be able to help out too!

Best of luck with the challenge everyone!!

[morgan]

FYI I just fixed a permissions issue with the Weights & Biases projects created so far if anyone had trouble logging to a project already created please try again, it should be working now

Check out the post above for more info on W&B Projects and feel free to drop a post here or DM me directly with any questions you have

[morgan]

Using W&B with the OVH run_common_voice.py script

Run the following in the terminal

# 1. Make sure you have the latest W&B and Transformers from master
pip install git+https://github.com/huggingface/transformers.git
pip install wandb --upgrade

# 2. login to wandb
wandb login

# 3. Set your Project name and Entity (no quotes needed around `wandb` for example) 
export WANDB_ENTITY = wandb
export WANDB_PROJECT = xlsr-french

# 4.  Save your model to W&B (optional)
export WANDB_LOG_MODEL = true 

Now you just have to add the usual wandb parameters that Trainer needs to the finetune.sh script:

python /workspace/wav2vec/run_common_voice.py \
    --report_to="wandb"
    --run_name = 'fr-baseline',   # Name your run, optional
    --load_best_model_at_end = True, 
    --metric_for_best_model='wer',  
    --greater_is_better=False,
    .....

[morgan]

Flat - Linear Learning Rate Schedule from Wav2Vec2 paper

Sharing Trainer code for the same learning rate schedule as the paper, 10% warmup, 40% flat, 50% linear decay

def get_flat_linear_schedule_with_warmup(optimizer:Optimizer, num_warmup_steps:int,
                                         num_training_steps:int, last_epoch:int =-1):
    def lr_lambda(current_step):
        constant_steps = int(num_training_steps * 0.4)
        warmup_steps = int(num_training_steps * 0.1)
        if current_step < warmup_steps:
            return float(current_step) / float(max(1, warmup_steps))
        elif current_step < warmup_steps+constant_steps:
            return 1
        else: return max(
            0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - (warmup_steps+constant_steps)))
        )
    return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_flat_cheduler(
    name: Union[str, SchedulerType] = None,
    optimizer: Optimizer = None,
    num_warmup_steps: Optional[int] = None,
    num_training_steps: Optional[int] = None,
):
    return get_flat_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, 
                                                num_training_steps=num_training_steps)

And creating Trainer warpper:

class FlatTrainer(Trainer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
    def create_flat_scheduler(self, num_training_steps: int):
        self.lr_scheduler = get_flat_cheduler(optimizer = self.optimizer,
                                              num_training_steps=num_training_steps)
    def create_optimizer_and_scheduler(self, num_training_steps):
        self.create_optimizer()
        self.create_flat_scheduler(num_training_steps)

[morgan]

Normalise by “loudness”

Code to normalise your audio by “loudness”, I only used this for the train set

import soundfile as sf
import pyloudnorm as pyln

def get_loudness_normalised(sa, sr):
    # peak normalize audio to -1 dB
    peak_normalized_audio = pyln.normalize.peak(sa, -1.0)

    # measure the loudness first 
    meter = pyln.Meter(sr) # create BS.1770 meter
    loudness = meter.integrated_loudness(sa)

    # loudness normalize audio to -12 dB LUFS
    loudness_normalized_audio = pyln.normalize.loudness(sa, loudness, -12.0)

    return loudness_normalized_audio

def speech_file_to_array_loud_norm_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    
    # DO loudness normalisation
    sa = get_loudness_normalised(speech_array[0].numpy(), sampling_rate)
    
    batch["speech"] = sa
    batch["sampling_rate"] = sampling_rate
    batch["target_text"] = batch["sentence"]
    return batch

And apply via map:

common_voice_train = common_voice_train.map(speech_file_to_array_loud_norm_fn)