Update README.md

README.md

@@ -27,4 +27,147 @@ This log is the output from Quntized  "Omarrran/quantized_english_speecht5_finet
 2024-10-22 09:40:49,200 - SpeechQuantizer - INFO - Model saved and pushed successfully
 
 
+```
+
+
+
+
+# Quantized SpeechT5 Model Details
+
+The provided information is about a quantized version of the SpeechT5 model, specifically the `Omarrran/quantized_english_speecht5_finetune-tts` model. 
+## Model Overview
+- The model is a SpeechT5ForSpeechToText model, which is a transformer-based model for speech-to-text tasks.
+- The model has a total of 153.07 million parameters.
+- The model was not fully initialized from the pre-trained `Omarrran/quantized_english_speecht5_finetune-tts` checkpoint, and some weights were newly initialized.
+
+## Model Architecture
+The model consists of two main components:
+
+1. **Encoder**:
+   - The encoder is an instance of `SpeechT5EncoderWithSpeechPrenet`, which includes a speech feature encoder, a feature projection layer, and a transformer-based encoder.
+   - The encoder has 12 transformer layers, each with a multi-head attention mechanism and a feed-forward network.
+   - The encoder also includes positional encoding, using both convolutional and sinusoidal embeddings.
+
+2. **Decoder**:
+   - The decoder is an instance of `SpeechT5DecoderWithTextPrenet`, which includes a text decoder prenet and a transformer-based decoder.
+   - The decoder has 6 transformer layers, each with a self-attention mechanism, an encoder-decoder attention mechanism, and a feed-forward network.
+   - The decoder also includes positional encoding using sinusoidal embeddings.
+```
+
+Some weights of SpeechT5ForSpeechToText were not initialized from the model checkpoint at Omarrran/quantized_english_speecht5_finetune-tts and are newly initialized: ['speecht5.decoder.wrapped_decoder.layers.0.encoder_attn.k_proj.bias', 'speecht5.decoder.wrapped_decoder.layers.0.encoder_attn.k_proj.weight', 'speecht5.decoder.wrapped_decoder.layers.0.encoder_attn.out_proj.bias', 'speecht5.decoder.wrapped_decoder.layers.0.encoder_attn.out_proj.weight', 'speecht5.decoder.wrapped_decoder.layers.0.encoder_attn.q_proj.bias', 'speecht5.decoder.wrapped_decoder.layers.0.encoder_attn.q_proj.weight', 'speecht5.decoder.wrapped_decoder.layers.0.encoder_attn.v_proj.bias', 'speecht5.decoder.wrapped_decoder.layers.0.encoder_attn.v_proj.weight', 'speecht5.decoder.wrapped_decoder.layers.0.feed_forward.intermediate_dense.bias', 'speecht5.decoder.wrapped_decoder.layers.0.feed_forward.intermediate_dense.weight', 'speecht5.decoder.wrapped_decoder.layers.0.feed_forward.output_dense.bias', 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+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+Model Size: 153.07 million parameters
+Model Details:
+SpeechT5ForSpeechToText(
+  (speecht5): SpeechT5Model(
+    (encoder): SpeechT5EncoderWithSpeechPrenet(
+      (prenet): SpeechT5SpeechEncoderPrenet(
+        (feature_encoder): SpeechT5FeatureEncoder(
+          (conv_layers): ModuleList(
+            (0): SpeechT5GroupNormConvLayer(
+              (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False)
+              (activation): GELUActivation()
+              (layer_norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+            )
+            (1-4): 4 x SpeechT5NoLayerNormConvLayer(
+              (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False)
+              (activation): GELUActivation()
+            )
+            (5-6): 2 x SpeechT5NoLayerNormConvLayer(
+              (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False)
+              (activation): GELUActivation()
+            )
+          )
+        )
+        (feature_projection): SpeechT5FeatureProjection(
+          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
+          (projection): Linear(in_features=512, out_features=768, bias=True)
+          (dropout): Dropout(p=0.0, inplace=False)
+        )
+        (pos_conv_embed): SpeechT5PositionalConvEmbedding(
+          (conv): ParametrizedConv1d(
+            768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16
+            (parametrizations): ModuleDict(
+              (weight): ParametrizationList(
+                (0): _WeightNorm()
+              )
+            )
+          )
+          (padding): SpeechT5SamePadLayer()
+          (activation): GELUActivation()
+        )
+        (pos_sinusoidal_embed): SpeechT5SinusoidalPositionalEmbedding()
+      )
+      (wrapped_encoder): SpeechT5Encoder(
+        (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+        (layers): ModuleList(
+          (0-11): 12 x SpeechT5EncoderLayer(
+            (attention): SpeechT5Attention(
+              (k_proj): Linear(in_features=768, out_features=768, bias=True)
+              (v_proj): Linear(in_features=768, out_features=768, bias=True)
+              (q_proj): Linear(in_features=768, out_features=768, bias=True)
+              (out_proj): Linear(in_features=768, out_features=768, bias=True)
+            )
+            (dropout): Dropout(p=0.1, inplace=False)
+            (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+            (feed_forward): SpeechT5FeedForward(
+              (intermediate_dropout): Dropout(p=0.1, inplace=False)
+              (intermediate_dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+              (output_dense): Linear(in_features=3072, out_features=768, bias=True)
+              (output_dropout): Dropout(p=0.1, inplace=False)
+            )
+            (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+          )
+        )
+        (embed_positions): SpeechT5RelativePositionalEncoding(
+          (pe_k): Embedding(320, 64)
+        )
+      )
+    )
+    (decoder): SpeechT5DecoderWithTextPrenet(
+      (prenet): SpeechT5TextDecoderPrenet(
+        (dropout): Dropout(p=0.1, inplace=False)
+        (embed_tokens): Embedding(81, 768, padding_idx=1)
+        (embed_positions): SpeechT5SinusoidalPositionalEmbedding()
+      )
+      (wrapped_decoder): SpeechT5Decoder(
+        (layers): ModuleList(
+          (0-5): 6 x SpeechT5DecoderLayer(
+            (self_attn): SpeechT5Attention(
+              (k_proj): Linear(in_features=768, out_features=768, bias=True)
+              (v_proj): Linear(in_features=768, out_features=768, bias=True)
+              (q_proj): Linear(in_features=768, out_features=768, bias=True)
+              (out_proj): Linear(in_features=768, out_features=768, bias=True)
+            )
+            (dropout): Dropout(p=0.1, inplace=False)
+            (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+            (encoder_attn): SpeechT5Attention(
+              (k_proj): Linear(in_features=768, out_features=768, bias=True)
+              (v_proj): Linear(in_features=768, out_features=768, bias=True)
+              (q_proj): Linear(in_features=768, out_features=768, bias=True)
+              (out_proj): Linear(in_features=768, out_features=768, bias=True)
+            )
+            (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+            (feed_forward): SpeechT5FeedForward(
+              (intermediate_dropout): Dropout(p=0.1, inplace=False)
+              (intermediate_dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+              (output_dense): Linear(in_features=3072, out_features=768, bias=True)
+              (output_dropout): Dropout(p=0.1, inplace=False)
+            )
+            (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+          )
+        )
+      )
+    )
+  )
+  (text_decoder_postnet): SpeechT5TextDecoderPostnet(
+    (lm_head): Linear(in_features=768, out_features=81, bias=False)
+  )
+)
+
+
+
 ```