Small Scream - April Beta

This model is a fine-tuned version of openai/whisper-small on the NbAiLab/NCC_speech_all_v5 dataset. It uses a Beam size of 5. It achieves the following results on the evaluation set:

step: 49999
eval_loss: 0.5299
train_loss: 0.3369
eval_wer: 11.9976
eval_cer: 5.6236

Model description

This is a BETA version. You need to accept the terms and conditons to use it.

Using the Model

There are several ways of using this model, and we do hope people will convert it into different formats. The code below allows you to process long files with Transformers.:

import torch
import numpy as np
import librosa
from transformers import pipeline

# Try using "mps" for Metal (Mac), "cuda" if you have GPU, and "cpu" if not
device = torch.device("cuda")

pipe = pipeline("automatic-speech-recognition",
      model="NbAiLab/small_scream_april_beta",
      chunk_length_s=30,
      device=device,
      max_new_tokens=128,
      generate_kwargs={"language": "", "task": "transcribe"})

# Load the WAV file. Modify this to use mp3 instead
audio_path = 'myfile.wav'
samples, sample_rate = librosa.load(audio_path, sr=16000, mono=True)

# Run the pipeline
prediction = pipe(samples)["text"]

print(prediction)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-06
lr_scheduler_type: linear
per_device_train_batch_size: 16
total_train_batch_size_per_node: 64
total_train_batch_size: 64
total_optimization_steps: 50000
starting_optimization_step: None
finishing_optimization_step: 50000
num_train_dataset_workers: 32
total_num_training_examples: 3200000

Training results

step	eval_loss	train_loss	eval_wer	eval_cer
0	1.5034	1.6162	32.7040	11.9022
0	1.5034	1.6129	32.7040	11.9022
0	1.5034	1.5855	32.7040	11.9022
2500	0.9684	0.6679	21.7113	8.0826
5000	0.8986	0.6577	18.6358	7.3167
7500	0.7365	0.4619	16.3825	6.8027
10000	0.6429	0.4965	14.7990	6.2887
12500	0.6688	0.4602	13.7942	6.0217
15000	0.6509	0.4650	13.3069	5.9965
17500	0.5692	0.3979	12.8502	5.6790
20000	0.5530	0.3931	13.0938	5.8554
22500	0.5320	0.4441	12.5457	5.7596
25000	0.5109	0.4116	12.7893	5.8503
27500	0.4855	0.3728	12.9111	5.8856
30000	0.4720	0.3842	12.6066	5.8201
32500	0.4889	0.3051	12.4239	5.7244
35000	0.5312	0.3388	12.6066	5.9259
37500	0.5138	0.3409	12.3934	5.7999
40000	0.5214	0.2886	11.9367	5.5530
42500	0.5420	0.3431	12.6675	5.9914
45000	0.5263	0.4015	12.3934	5.9360
47500	0.5378	0.3218	12.1194	5.6185
49999	0.5299	0.3369	11.9976	5.6236

Framework versions

Transformers 4.28.0.dev0
Datasets 2.11.0
Tokenizers 0.13.3

NbAiLabArchive
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small_scream_april_beta