ie_gp1800_transformer

Usage

import librosa
from espnet2.bin.asr_inference import Speech2Text

speech2text = Speech2Text.from_pretrained(model_tag="espnet/ie_gp1800_transformer")
# librosa resamples and mixes to one channel, so any file works; 16000 is
# what nearly every espnet recogniser is trained on - check this model's
# config if its audio is not 16 kHz
speech, rate = librosa.load("audio.wav", sr=16000, mono=True)
text, *_ = speech2text(speech)[0]
print(text)

Model Card for Indian English ASR

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ASR model built by Speech Lab, IIT Madras

Table of Contents

• Model Card for Indian English ASR
• Table of Contents
• Table of Contents
• Model Details
  • Model Description
• Uses
  • Direct Use
  • Downstream Use [Optional]
  • Out-of-Scope Use
• Bias, Risks, and Limitations
  • Recommendations
• Training Details
  • Training Data
  • Training Procedure
    • Preprocessing
    • Speeds, Sizes, Times
• Evaluation
  • Testing Data, Factors & Metrics
    • Testing Data
    • Factors
    • Metrics
  • Results
• Model Examination
• Environmental Impact
• Technical Specifications [optional]
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
• Citation
• Glossary [optional]
• More Information [optional]
• Model Card Authors [optional]
• Model Card Contact
• How to Get Started with the Model

Model Details

Model Description

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ASR model built by Speech Lab, IIT Madras

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• Shared by [Optional]: More information needed
• Model type: Language model
• Language(s) (NLP): en
• License: apache-2.0
• Parent Model: More information needed
• Resources for more information: More information needed

Uses

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Direct Use

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Downstream Use [Optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

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Training Details

Training Data

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Training Procedure

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Preprocessing

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Speeds, Sizes, Times

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More information needed

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Model Examination

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: More information needed
• Hours used: More information needed
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• Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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How to Get Started with the Model

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<details> <summary> Click to expand </summary>

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</details>