Update README.md

README.md

@@ -18,67 +18,100 @@ Classifies voice input into 11 English Accents
 
 ## Model Details
 
-### Model Description
+This model is a finetune of facebook/mms-lid-256 on the [speech accent archive dataset](https://accent.gmu.edu/)
 
-<!-- Provide a longer summary of what this model is. -->
+It classies voice into 11 English Accents:\
+        "0": "African"\
+        "1": "Australian"\
+        "2": "British"\
+        "3": "EastAsian"\
+        "4": "EasternEuropean"\
+        "5": "LatinAmerican"\
+        "6": "MiddleEastern"\
+        "7": "NorthAmerican"\
+        "8": "SouthAsian"\
+        "9": "SouthEastAsian"\
+        "10": "WesternEuropean"
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
+Because of the constraints of the dataset, the input audio should be saying the phrase for best prediction results:
+
+> Please call Stella. Ask her to bring these things with her from the store: Six spoons of fresh snow peas, five thick slabs of blue cheese, and maybe a snack for her brother Bob. We also need a small plastic snake and a big toy frog for the kids. She can scoop these things into three red bags, and we will go meet her Wednesday at the train station.
+
+
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+You can load the model using the ID vkao8264/mms-accent-predict with the Transformers package
+
+```python
+from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
+import torchaudio
+import torch
 
-### Downstream Use [optional]
+def load_and_preprocess_audio(path):
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+    waveform, sr = torchaudio.load(path)
 
-[More Information Needed]
+    # Resample to 16kHz because mms uses Wav2Vec
+    if sr != sample_rate:
+        waveform = torchaudio.transforms.Resample(orig_freq=sr, new_freq=sample_rate)(waveform)
 
-### Out-of-Scope Use
+    # Convert to mono if stereo
+    if waveform.shape[0] > 1:
+        waveform = waveform.mean(dim=0, keepdim=True)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+    # Remove channel dimension and convert to 1D
+    waveform = waveform.squeeze(0)
 
-[More Information Needed]
+    inputs = feature_extractor(
+        waveform,
+        sampling_rate=sample_rate,
+        return_tensors="pt",
+        padding="max_length",
+        max_length=sample_rate * max_audio_length,
+        truncation=True
+    )
 
-## Bias, Risks, and Limitations
+    return inputs.input_values
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+id_to_class = {
+  0: "African",
+  1: "Australian",
+  2: "British",
+  3: "EastAsian",
+  4: "EasternEuropean",
+  5: "LatinAmerican",
+  6: "MiddleEastern",
+  7: "NorthAmerican",
+  8: "SouthAsian",
+  9: "SouthEastAsian",
+  10: "WesternEuropean" 
+}
 
-[More Information Needed]
+sample_rate = 16000
+max_audio_length = 15
 
-### Recommendations
+model = AutoModelForAudioClassification.from_pretrained("vkao8264/mms-accent-predict")
+feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/mms-lid-256")
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+sample = "audio_input.mp3"
+inputs = load_and_preprocess_audio(sample)
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+predictions = model(inputs)
+pred_label = torch.argmax(predictions['logits']).item()
 
-## How to Get Started with the Model
+print(id_to_class[pred_label])
 
-Use the code below to get started with the model.
+```
 
-[More Information Needed]
 
 ## Training Details
 
@@ -86,121 +119,13 @@ Use the code below to get started with the model.
 
 <!-- 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. -->
 
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+The whole training data consists of about 2000 unique audio samples from the speech accent archive, downloaded from [kaggle](https://www.kaggle.com/datasets/rtatman/speech-accent-archive/data)
+Data is then further split into training and validation set of size 1698 and 425 respectively
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+Accuracy on the validation set: 0.86 (f1 score)
 
-[More Information Needed]
+![c](mms_eval.png)