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wav2vec2-large-xlsr-53-finnish

Automatic Speech Recognition Transformers PyTorch JAX
Finnish wav2vec2 audio speech xlsr-fine-tuning-week Eval Results Inference Endpoints
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metadata
language: fi
datasets:
  - common_voice
metrics:
  - wer
  - cer
tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
license: apache-2.0
model-index:
  - name: XLSR Wav2Vec2 Finnish by Jonatas Grosman
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice fi
          type: common_voice
          args: fi
        metrics:
          - name: Test WER
            type: wer
            value: 41.6
          - name: Test CER
            type: cer
            value: 8.23

Fine-tuned XLSR-53 large model for speech recognition in Finnish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz.

This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :)

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

Usage

The model can be used directly (without a language model) as follows...

Using the HuggingSound library:

from huggingsound import SpeechRecognitionModel

model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-finnish")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]

transcriptions = model.transcribe(audio_paths)

Writing your own inference script:

import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "fi"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-finnish"
SAMPLES = 5

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)

for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)
Reference Prediction
MYSTEERIMIES OLI OPPINUT MORAALINSA TARUISTA, ELOKUVISTA JA PELEISTÄ. MYSTEERIMIES OLI OPPINUT MORALINSA TARUISTA ELOKUVISTA JA PELEISTÄ
ÄÄNESTIN MIETINNÖN PUOLESTA! ÄÄNESTIN MIETINNÖN PUOLESTA
VAIN TUNTIA AIKAISEMMIN OLIMME MIEHENI KANSSA TUNTENEET SUURINTA ILOA. PAIN TUNTIA AIKAISEMMIN OLIN MIEHENI KANSSA TUNTENEET SUURINTA ILAA
ENSIMMÄISELLE MIEHELLE SAI KOLME LASTA. ENSIMMÄISELLE MIEHELLE SAI KOLME LASTA
ÄÄNESTIN MIETINNÖN PUOLESTA, SILLÄ POHJIMMILTAAN SIINÄ VASTUSTETAAN TÄTÄ SUUNTAUSTA. ÄÄNESTIN MIETINNÖN PUOLESTA SILLÄ POHJIMMILTAAN SIINÄ VASTOTTETAAN TÄTÄ SUUNTAUSTA
TÄHDENLENTOJENKO VARALTA MINÄ SEN OLISIN TÄNNE KUSKANNUT? TÄHDEN LENTOJENKO VARALTA MINÄ SEN OLISIN TÄNNE KUSKANNUT
SIITÄ SE TULEE. SIITA SE TULEE
NIIN, KUULUU KIROUS, JA KAUHEA KARJAISU. NIIN KUULUU KIROUS JA KAUHEA KARJAISU
ARKIT KUN OVAT NÄES ELEMENTTIRAKENTEISIA. ARKIT KUN OVAT MÄISS' ELÄMÄTTEROKENTEISIÄ
JÄIN ALUKSEN SISÄÄN, MUTTA KUULIN OVEN LÄPI, ETTÄ ULKOPUOLELLA ALKOI TAPAHTUA. JAKALOKSEHÄN SISÄL MUTTA KUULIN OVENLAPI ETTÄ ULKA KUOLLALLA ALKOI TAPAHTUA

Evaluation

The model can be evaluated as follows on the Finnish test data of Common Voice.

import torch
import re
import librosa
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "fi"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-finnish"
DEVICE = "cuda"

CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
                   "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
                   "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
                   "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
                   "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]

test_dataset = load_dataset("common_voice", LANG_ID, split="test")

wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py

chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

predictions = [x.upper() for x in result["pred_strings"]]
references = [x.upper() for x in result["sentence"]]

print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")

Test Result:

In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-04-21). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.

Model WER CER
aapot/wav2vec2-large-xlsr-53-finnish 32.51% 5.34%
Tommi/wav2vec2-large-xlsr-53-finnish 35.22% 5.81%
vasilis/wav2vec2-large-xlsr-53-finnish 38.24% 6.49%
jonatasgrosman/wav2vec2-large-xlsr-53-finnish 41.60% 8.23%
birgermoell/wav2vec2-large-xlsr-finnish 53.51% 9.18%

Citation

If you want to cite this model you can use this:

@misc{grosman2021xlsr53-large-finnish,
  title={Fine-tuned {XLSR}-53 large model for speech recognition in {F}innish},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-finnish}},
  year={2021}
}