Jonatasgrosman/wav2vec2-large-xlsr-53-english model: Difference between revisions

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(Created page with "{{Model infobox | hugging-face-uri = jonatasgrosman/wav2vec2-large-xlsr-53-english | creator = | type = Audio | task = Automatic Speech Recognition | library = PyTorch, JAX, Safetensors, Transformers | dataset = common_voice, mozilla-foundation/common_voice_6_0 | language = English | paper = | related-to = wav2vec2, audio, hf-asr-leaderboard, mozilla-foundation/common_voice_6_0, robust-speech-event, speech, xlsr-fine-tuning-week | license = apache-2.0 | all-tags = Auto...")
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Revision as of 03:45, 2 May 2023

Jonatasgrosman/wav2vec2-large-xlsr-53-english model

Jonatasgrosman/wav2vec2-large-xlsr-53-english model is a Audio model used for Automatic Speech Recognition.

Clone Model Repository

HTTPS

#Be sure to have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english
  
#To clone the repo without large files – just their pointers
#prepend git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1

SSH

#Be sure to have git-lfs installed (https://git-lfs.com)
git lfs install
git clone [email protected]:jonatasgrosman/wav2vec2-large-xlsr-53-english
  
#To clone the repo without large files – just their pointers
#prepend git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1


Hugging Face Transformers Library

from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-english")

model = AutoModelForCTC.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-english")


Deployment

Inference API

Python

import requests

API_URL = "https://api-inference.huggingface.co/models/jonatasgrosman/wav2vec2-large-xlsr-53-english"
headers = {"Authorization": f"Bearer {API_TOKEN}"}

def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

output = query("sample1.flac")

JavaScript

async function query(filename) {
	const data = fs.readFileSync(filename);
	const response = await fetch(
		"https://api-inference.huggingface.co/models/jonatasgrosman/wav2vec2-large-xlsr-53-english",
		{
			headers: { Authorization: "Bearer {API_TOKEN}" },
			method: "POST",
			body: data,
		}
	);
	const result = await response.json();
	return result;
}

query("sample1.flac").then((response) => {
	console.log(JSON.stringify(response));
});

cURL

curl https://api-inference.huggingface.co/models/jonatasgrosman/wav2vec2-large-xlsr-53-english \
	-X POST \
	--data-binary '@sample1.flac' \
	-H "Authorization: Bearer {API_TOKEN}"

Amazon SageMaker

Automatic Speech Recognition

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'image-classification'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'image-classification'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Conversational

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'conversational'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'conversational'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Feature Extraction

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'feature-extraction'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
OLD MODEL INFOBOX

Fill-Mask

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'fill-mask'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'fill-mask'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Image Classification

AWS
OLD MODEL INFOBOX
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'image-classification'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Question Answering

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'question-answering'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'question-answering'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Summarization

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'summarization'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'summarization'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Table Question Answering

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'table-question-answering'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'table-question-answering'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Text Classification

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'text-classification'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
OLD MODEL INFOBOX

Text Generation

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'text-generation'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'text-generation'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Text2Text Generation

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'text2text-generation'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'text2text-generation'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Token Classification

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'token-classification'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'token-classification'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Translation

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'translation'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'translation'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Zero-Shot Classification

AWS
from sagemaker.huggingface import HuggingFaceModel
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'zero-shot-classification'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})
Local Machine
from sagemaker.huggingface import HuggingFaceModel
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'HF_TASK':'zero-shot-classification'
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	env=hub,
	role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
	initial_instance_count=1, # number of instances
	instance_type='ml.m5.xlarge' # ec2 instance type
)

predictor.predict({
	'inputs': "sample1.flac"
})

Spaces

import gradio as gr

gr.Interface.load("models/jonatasgrosman/wav2vec2-large-xlsr-53-english").launch()


Training

Amazon SageMaker

Causal Language Modeling

AWS
import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_clm.py',
	source_dir='./examples/pytorch/language-modeling',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
Local Machine
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_clm.py',
	source_dir='./examples/pytorch/language-modeling',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()

Masked Language Modeling

AWS
import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_mlm.py',
	source_dir='./examples/pytorch/language-modeling',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
Local Machine
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_mlm.py',
	source_dir='./examples/pytorch/language-modeling',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()

Question Answering

AWS
import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/question-answering
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_qa.py',
	source_dir='./examples/pytorch/question-answering',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
Local Machine
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/question-answering
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_qa.py',
	source_dir='./examples/pytorch/question-answering',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()

Summarization

AWS
import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_summarization.py',
	source_dir='./examples/pytorch/seq2seq',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
Local Machine
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_summarization.py',
	source_dir='./examples/pytorch/seq2seq',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()

Text Classification

AWS
import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/text-classification
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_glue.py',
	source_dir='./examples/pytorch/text-classification',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
Local Machine
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/text-classification
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_glue.py',
	source_dir='./examples/pytorch/text-classification',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()

Token Classification

AWS
import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/token-classification
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_ner.py',
	source_dir='./examples/pytorch/token-classification',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
Local Machine
{{Model infobox

Translation

AWS
import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_translation.py',
	source_dir='./examples/pytorch/seq2seq',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
Local Machine
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
hyperparameters = {
	'model_name_or_path':'jonatasgrosman/wav2vec2-large-xlsr-53-english',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_translation.py',
	source_dir='./examples/pytorch/seq2seq',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.17.0',
	pytorch_version='1.10.2',
	py_version='py38',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()


Model Card

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