add README

README.md

@@ -0,0 +1,83 @@
+---
+language: en
+datasets:
+- librispeech
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- asr
+- hubert
+license: apache-2.0
+metrics:
+- wer
+- cer
+---
+
+# voidful/asr_hubert_cluster_bart_base
+
+
+## Usage
+download file
+```shell
+wget https://raw.githubusercontent.com/voidful/hubert-cluster-code/main/km_feat_100_layer_20
+wget https://cdn-media.huggingface.co/speech_samples/sample1.flac
+```
+
+Hubert kmeans code
+```python
+import joblib
+import torch
+from transformers import Wav2Vec2FeatureExtractor, HubertModel
+import soundfile as sf
+
+
+class HubertCode(object):
+    def __init__(self, hubert_model, km_path, km_layer):
+        self.processor = Wav2Vec2FeatureExtractor.from_pretrained(hubert_model)
+        self.model = HubertModel.from_pretrained(hubert_model)
+        self.km_model = joblib.load(km_path)
+        self.km_layer = km_layer
+        self.C_np = self.km_model.cluster_centers_.transpose()
+        self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True)
+
+        self.C = torch.from_numpy(self.C_np)
+        self.Cnorm = torch.from_numpy(self.Cnorm_np)
+        if torch.cuda.is_available():
+            self.C = self.C.cuda()
+            self.Cnorm = self.Cnorm.cuda()
+            self.model = self.model.cuda()
+
+    def __call__(self, filepath, sampling_rate=None):
+        speech, sr = sf.read(filepath)
+        input_values = self.processor(speech, return_tensors="pt", sampling_rate=sr).input_values
+        if torch.cuda.is_available():
+            input_values = input_values.cuda()
+        hidden_states = self.model(input_values, output_hidden_states=True).hidden_states
+        x = hidden_states[self.km_layer].squeeze()
+        dist = (
+                x.pow(2).sum(1, keepdim=True)
+                - 2 * torch.matmul(x, self.C)
+                + self.Cnorm
+        )
+        return dist.argmin(dim=1).cpu().numpy()
+```
+input
+```python
+hc = HubertCode("facebook/hubert-large-ll60k", './km_feat_100_layer_20', 20)
+voice_ids = hc('./sample1.flac')
+```
+bart model
+````python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("voidful/asr_hubert_cluster_bart_base")
+model = AutoModelForSeq2SeqLM.from_pretrained("voidful/asr_hubert_cluster_bart_base")
+````
+generate output
+```python
+gen_output = model.generate(input_ids=tokenizer("".join([f":vtok{i}:" for i in voice_ids]),return_tensors='pt').input_ids,max_length=1024)
+print(tokenizer.decode(gen_output[0], skip_special_tokens=True))
+```
+
+## Result
+`going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to ask immediately afterwards`