Add a quantized variant of whisper (#1017)

* Add the quantized-whisper model.

* Quantized the whisper model.

* Adapt the whisper example to handle quantization.

* Add the quantized flag.

* Load the proper weights.

candle-transformers/src/models/whisper/mod.rs

@@ -1,5 +1,25 @@
 pub mod audio;
 pub mod model;
+pub mod quantized_model;
+
+use serde::Deserialize;
+
+// The names in comments correspond to the original implementation:
+// https://github.com/openai/whisper/blob/f572f2161ba831bae131364c3bffdead7af6d210/whisper/model.py#L17
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct Config {
+    pub num_mel_bins: usize,            // n_mels
+    pub max_source_positions: usize,    // n_audio_ctx
+    pub d_model: usize,                 // n_audio_state
+    pub encoder_attention_heads: usize, // n_audio_head
+    pub encoder_layers: usize,          // n_audio_layer
+    pub vocab_size: usize,              // n_vocab
+    pub max_target_positions: usize,    //  n_text_ctx
+    // pub n_text_state: usize,
+    pub decoder_attention_heads: usize, // n_text_head
+    pub decoder_layers: usize,          // n_text_layer
+    pub suppress_tokens: Vec<u32>,
+}
 
 pub const DTYPE: candle::DType = candle::DType::F32;