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library_name: transformers
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---
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# Model Card
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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license: mit
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language: fr
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datasets:
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- Cnam-LMSSC/vibravox
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metrics:
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- per
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tags:
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- audio
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- automatic-speech-recognition
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- speech
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- phonemize
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- phoneme
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model-index:
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- name: Wav2Vec2-base French finetuned for Speech-to-Phoneme by LMSSC
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results:
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- task:
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name: Speech-to-Phoneme
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type: automatic-speech-recognition
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dataset:
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name: Vibravox["forehead_accelerometer"]
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type: Cnam-LMSSC/vibravox
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args: fr
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metrics:
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- name: Test PER, in-domain training |
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type: per
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value: 4.5
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# Model Card
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- **Developed by:** [Cnam-LMSSC](https://huggingface.co/Cnam-LMSSC)
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- **Model type:** [Wav2Vec2ForCTC](https://huggingface.co/transformers/v4.9.2/model_doc/wav2vec2.html#transformers.Wav2Vec2ForCTC)
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- **Language:** French
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- **License:** MIT
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- **Finetuned from model:** [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2)
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- **Finetuned dataset:** `forehead_accelerometer` audio of the `speech_clean` subset of [Cnam-LMSSC/vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox)
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- **Samplerate for usage:** 16kHz
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/KkZoQQmrn53U6BTLmr0XK.png" />
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</p>
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## Output
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As this model is specifically trained for a speech-to-phoneme task, the output is sequence of [IPA-encoded](https://en.wikipedia.org/wiki/International_Phonetic_Alphabet) words, without punctuation.
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If you don't read the phonetic alphabet fluently, you can use this excellent [IPA reader website](http://ipa-reader.xyz) to convert the transcript back to audio synthetic speech in order to check the quality of the phonetic transcription.
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## Link to other phonemizer models trained on other body conducted sensors :
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An entry point to all **phonemizers** (speech-to-phoneme ASR) models trained on different sensor data from the trained on different sensor data from the [Vibravox dataset](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) is available at [https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers](https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers).
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### Disclaimer
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Each of these models has been trained for a **specific non-conventional speech sensor** and is intended to be used with **in-domain data**. The only exception is the headset microphone phonemizer, which can certainly be used for many applications using audio data captured by airborne microphones.
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Please be advised that using these models outside their intended sensor data may result in suboptimal performance.
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## Training procedure
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The model has been finetuned for 10 epochs with a constant learning rate of *1e-5*. To reproduce experiment please visit [jhauret/vibravox](https://github.com/jhauret/vibravox).
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## Inference script :
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```python
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import torch, torchaudio
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from transformers import AutoProcessor, AutoModelForCTC
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from datasets import load_dataset
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processor = AutoProcessor.from_pretrained("Cnam-LMSSC/phonemizer_forehead_accelerometer")
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model = AutoModelForCTC.from_pretrained("Cnam-LMSSC/phonemizer_forehead_accelerometer")
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test_dataset = load_dataset("Cnam-LMSSC/vibravox", "speech_clean", split="test", streaming=True)
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audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.forehead_accelerometer"]["array"])
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audio_16kHz = torchaudio.functional.resample(audio_48kHz, orig_freq=48_000, new_freq=16_000)
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inputs = processor(audio_16kHz, sampling_rate=16_000, return_tensors="pt")
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logits = model(inputs.input_values).logits
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predicted_ids = torch.argmax(logits,dim = -1)
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transcription = processor.batch_decode(predicted_ids)
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print("Phonetic transcription : ", transcription)
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```
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