no longer log tensors

deployment.yaml

@@ -93,7 +93,7 @@ spec:
         # The following image runs a rudimentary Arbapet engine, but doesn't
         # handle puncutation and tokenization
         #image: docker.pkg.github.com/ml-applications/voder/tts-service:6e766720ca7d
-        image: docker.pkg.github.com/ml-applications/voder/tts-service:7e4d663f73bf
+        image: docker.pkg.github.com/ml-applications/voder/tts-service:2dcc4e589669
         volumeMounts:
         - name: mntdatas3fs
           mountPath: /data:shared

tts_service/src/endpoints/legacy_speak.rs

@@ -28,7 +28,6 @@ pub async fn legacy_get_speak(_request: HttpRequest,
   query: Query<LegacyGetSpeakRequest>,
   app_state: Data<Arc<AppState>>
 ) -> std::io::Result<HttpResponse> {
-  println!("GET /speak");
 
   let speaker = match query.v.as_ref() {
     None => {
@@ -73,14 +72,12 @@ pub async fn legacy_get_speak(_request: HttpRequest,
           .map(|s| s.clone())
           .expect("TODO ERROR HANDLING");
 
-      println!("Tacotron Model: {}", tacotron_model);
-      println!("Melgan Model: {}", melgan_model);
+      //println!("Tacotron Model: {}", tacotron_model);
+      //println!("Melgan Model: {}", melgan_model);
 
       let arpabet = Arpabet::load_cmudict();
       let encoded = text_to_arpabet_encoding(arpabet, &text);
 
-      println!("Encoded Text: {:?}", encoded);
-
       let tacotron = app_state.model_cache.get_or_load_arbabet_tacotron(&tacotron_model).unwrap();
       let melgan = app_state.model_cache.get_or_load_melgan(&melgan_model).unwrap();
 

tts_service/src/old_model.rs

@@ -60,23 +60,14 @@ impl TacoMelModel {
 
   fn encoded_text_to_audio_signal(&self, tacotron: &ArpabetTacotronModel, melgan: &MelganModel, text_buffer: &Vec<i64>) -> Vec<i16> {
     let mut mel_tensor = tacotron.encoded_arpabet_to_mel(&text_buffer);
-    println!("\n\n>>> Mel tensor:\n{:?}\n\n", mel_tensor);
-    println!("Running melgan...");
     let audio_tensor = melgan.tacotron_mel_to_audio(&mel_tensor);
-    println!("\n\n>>> Audio tensor:\n{:?}\n\n", audio_tensor);
     Self::audio_tensor_to_audio_signal(audio_tensor)
   }
 
   fn audio_tensor_to_audio_signal(mel: Tensor) -> Vec<i16> {
     let mut flat_audio_tensor = mel.squeeze();
 
-    println!("Sqeueezed tensor: {:?}, dim: {}",
-      flat_audio_tensor,
-      flat_audio_tensor.dim());
-
     let length = flat_audio_tensor.size1().unwrap() as usize;
-    println!("Length: {}", length);
-
     let mut data : Vec<f32> = Vec::with_capacity(length);
 
     for i in 0 .. length {