fix

deployment.yaml

@@ -84,13 +84,15 @@ spec:
         # LSpeech model that sounds 100% amazing
         # image: docker.pkg.github.com/echelon/voder/tts-service:6b5c22567614
         #
-        image: docker.pkg.github.com/ml-applications/voder/tts-service:2bafa7cec0c5
+        image: docker.pkg.github.com/ml-applications/voder/tts-service:6f6101fa7730
         resources:
           limits:
-            memory: "2048Mi" # 2048 MiB = 2 GiB
+            #memory: "2048Mi" # 2048 MiB = 2 GiB
+            memory: "1048Mi" # 2048 MiB = 2 GiB
             cpu: "700m" # 700 milliCPU
           requests:
-            memory: "2048Mi" # 2048 MiB = 2 GiB
+            #memory: "2048Mi" # 2048 MiB = 2 GiB
+            memory: "1048Mi" # 2048 MiB = 2 GiB
             cpu: "700m" # 700 milliCPU
         volumeMounts:
         - name: downloads

tts_service/deployed_models.toml

@@ -4,8 +4,9 @@ name = "Donald Trump"
 slug = "trump"
 model_pipeline = "ArpabetTacotronMelgan"
 tacotron = "tacotron2_trump_txlearn_ljs_arpabet_2020-05-25_chkpt26500.jit"
-#melgan = "melgan_trump-txlearn-2020.05.05_13675.jit"
-melgan = "melgan_trump_txlearn_2020-05-30_steno_chkpt9800.torchjit"
+#melgan = "melgan_trump-txlearn-2020.05.05_13675.jit" (OLD)
+#melgan = "melgan_trump_txlearn_2020-05-30_steno_chkpt9800.torchjit" (BEST TACOTRON)
+glow_tts = "glow_tts_ljs_txlearn_arpabet_2020-06-01_G_1007.torchjit"
 
 [[speakers]]
 name = "High-pitched Linda Johnson"
@@ -20,6 +21,10 @@ melgan = "melgan_ljs-high-pitch_container.pt"
 model_type = "ArpabetTacotron"
 base_directory = "/data/downloads/downloaded/models/tacotron2-nvidia/"
 
+[[model_locations]]
+model_type = "ArpabetGlowTts"
+base_directory = "/data/downloads/downloaded/models/glow-tts-jaywalnut/"
+
 [[model_locations]]
 model_type = "Melgan"
 base_directory = "/data/downloads/downloaded/models/melgan-swpark/"
@@ -103,6 +108,13 @@ description = "High-quality high-pitched LJS; arpabet encodings."
 # description = "Unknown (broken)"
 
 
+# ---------------- Glow TTS----------------
+
+[[glow_tts]]
+file_path = "glow_tts_ljs_txlearn_arpabet_2020-06-01_G_1007.torchjit"
+description = "First experiments"
+
+
 # ---------------- Melgan ----------------
 
 # TODO: Get a high-quality LJS Melgan

tts_service/models.toml

@@ -138,7 +138,7 @@ description = "Unknown (broken)"
 # ---------------- Glow TTS----------------
 
 [[glow_tts]]
-file_path = "glow-experimental.torchjit"
+file_path = "glow_tts_ljs_txlearn_arpabet_2020-06-01_G_1007.torchjit"
 description = "First experiments"
 
 # ---------------- Melgan ----------------

tts_service/src/endpoints/legacy_speak.rs

@@ -13,7 +13,7 @@ use crate::model::model_config::ModelPipeline;
 use crate::model::old_model::TacoMelModel;
 use crate::database::model::NewSentence;
 use crate::text::cleaners::clean_text;
-use crate::model::pipelines::arpabet_tacotron_melgan_pipeline;
+use crate::model::pipelines::{arpabet_tacotron_melgan_pipeline, arpabet_glow_tts_melgan_pipeline};
 
 /// Example request: v=trump&vol=3&s=this is funny isn't it
 #[derive(Deserialize)]
@@ -152,7 +152,46 @@ pub async fn legacy_get_speak(request: HttpRequest,
         },
       }
     },
-    ModelPipeline::ArpabetGlowTtsMelgan => unimplemented!(),
+    ModelPipeline::ArpabetGlowTtsMelgan => {
+      let glow_tts_model = speaker.glow_tts
+          .as_ref()
+          .map(|s| s.clone())
+          .expect("TODO ERROR HANDLING");
+
+      let melgan_model = speaker.melgan
+          .as_ref()
+          .map(|s| s.clone())
+          .expect("TODO ERROR HANDLING");
+
+      debug!("Glow-TTS Model: {}", glow_tts_model);
+      debug!("Melgan Model: {}", melgan_model);
+
+      let glow_tts = match app_state.model_cache.get_or_load_arbabet_glow_tts(&glow_tts_model) {
+        Some(model) => model,
+        None => {
+          warn!("Couldn't load glow-tts model: {}", glow_tts_model);
+          return Ok(HttpResponse::build(StatusCode::INTERNAL_SERVER_ERROR)
+              .content_type("text/plain")
+              .body("Couldn't load model."));
+        },
+      };
+
+      let melgan = match app_state.model_cache.get_or_load_melgan(&melgan_model) {
+        Some(model) => model,
+        None => {
+          warn!("Couldn't load melgan model: {}", melgan_model);
+          return Ok(HttpResponse::build(StatusCode::INTERNAL_SERVER_ERROR)
+              .content_type("text/plain")
+              .body("Could not load model."));
+        },
+      };
+
+      let wav_data = arpabet_glow_tts_melgan_pipeline(&cleaned_text, &glow_tts, &melgan);
+
+      Ok(HttpResponse::build(StatusCode::OK)
+          .content_type("audio/wav")
+          .body(wav_data))
+    },
     ModelPipeline::RawTextTacotronMelgan => unimplemented!(),
   }
 }

tts_service/src/endpoints/speak.rs

@@ -111,22 +111,11 @@ pub async fn post_speak(_request: HttpRequest,
       let melgan = app_state.model_cache.get_or_load_melgan(&melgan_model)
           .expect(&format!("Couldn't load melgan model: {}", melgan_model));
 
-      let result = arpabet_glow_tts_melgan_pipeline(&cleaned_text, &glow_tts, &melgan);
+      let wav_data = arpabet_glow_tts_melgan_pipeline(&cleaned_text, &glow_tts, &melgan);
 
-      match result {
-        None => {
-          Ok(HttpResponse::build(StatusCode::TOO_MANY_REQUESTS)
-              .content_type("text/plain")
-              .body("The service is receiving too many requests. Although there are many worker \
-                     containers, model access is serialized on a per-container basis until the \
-                     segfaults are fixed."))
-        },
-        Some(wav_data) => {
-          Ok(HttpResponse::build(StatusCode::OK)
-              .content_type("audio/wav")
-              .body(wav_data))
-        },
-      }
+      Ok(HttpResponse::build(StatusCode::OK)
+          .content_type("audio/wav")
+          .body(wav_data))
     },
     ModelPipeline::RawTextTacotronMelgan => unimplemented!(),
   }

tts_service/src/model/pipelines.rs

@@ -17,7 +17,7 @@ use crate::model::arpabet_glow_tts_model::ArpabetGlowTtsModel;
 pub fn arpabet_glow_tts_melgan_pipeline(
   cleaned_text: &str,
   arpabet_glow_tts: &ArpabetGlowTtsModel,
-  melgan: &MelganModel) -> Option<Vec<u8>> {
+  melgan: &MelganModel) -> Vec<u8> {
 
   let arpabet = Arpabet::load_cmudict(); // TODO: Inefficient.
   let arpabet_encodings = text_to_arpabet_encoding_glow_tts(arpabet, &cleaned_text);
@@ -27,8 +27,7 @@ pub fn arpabet_glow_tts_melgan_pipeline(
   let audio_tensor = melgan.tacotron_mel_to_audio(&mel_tensor);
   let audio_signal = mel_audio_tensor_to_audio_signal(audio_tensor);
 
-  let wav = audio_signal_to_wav_bytes(audio_signal);
-  Some(wav)
+  audio_signal_to_wav_bytes(audio_signal)
 }
 
 pub fn arpabet_tacotron_melgan_pipeline(