Cnam-LMSSC
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phonemizer_temple_vibration_pickup

@@ -1,199 +1,83 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: mit
+language: fr
+datasets:
+- Cnam-LMSSC/vibravox
+metrics:
+- per
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- phonemize
+- phoneme
+model-index:
+- name: Wav2Vec2-base French finetuned for Speech-to-Phoneme by LMSSC
+  results:
+  - task:
+      name: Speech-to-Phoneme
+      type: automatic-speech-recognition
+    dataset:
+      name: Vibravox["body_conducted.temple.contact_microphone"]
+      type: Cnam-LMSSC/vibravox
+      args: fr
+    metrics:
+    - name: Test PER on Vibravox["body_conducted.temple.contact_microphone"] | Trained
+      type: per
+      value: 14.2
 ---
+# Model Card
+- **Developed by:** [Cnam-LMSSC](https://huggingface.co/Cnam-LMSSC)
+- **Model type:** [Wav2Vec2ForCTC](https://huggingface.co/transformers/v4.9.2/model_doc/wav2vec2.html#transformers.Wav2Vec2ForCTC)
+- **Language:** French
+- **License:** MIT
+- **Finetuned from model:** [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2)
+- **Finetuned dataset:** `temple_vibration_pickup` audio of the `speech_clean` subset of [Cnam-LMSSC/vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox)
+- **Samplerate for usage:** 16kHz
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/6390fc80e6d656eb421bab69/KkZoQQmrn53U6BTLmr0XK.png" />
+</p>
+## Output
+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.
+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.
+## Link to other phonemizer models trained on other body conducted sensors :
+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).
+### Disclaimer
+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.
+Please be advised that using these models outside their intended sensor data may result in suboptimal performance.
+## Training procedure
+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).
+## Inference script :
+```python
+import torch, torchaudio
+from transformers import AutoProcessor, AutoModelForCTC
+from datasets import load_dataset
+processor = AutoProcessor.from_pretrained("Cnam-LMSSC/phonemizer_temple_vibration_pickup")
+model = AutoModelForCTC.from_pretrained("Cnam-LMSSC/phonemizer_temple_vibration_pickup")
+test_dataset = load_dataset("Cnam-LMSSC/vibravox", "speech_clean", split="test", streaming=True)
+audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.temple_vibration_pickup"]["array"])
+audio_16kHz = torchaudio.functional.resample(audio_48kHz, orig_freq=48_000, new_freq=16_000)
+inputs = processor(audio_16kHz, sampling_rate=16_000, return_tensors="pt")
+logits = model(inputs.input_values).logits
+predicted_ids = torch.argmax(logits,dim = -1)
+transcription = processor.batch_decode(predicted_ids)
+print("Phonetic transcription : ", transcription)
+```