feat(transformers): merge musicgen functionalities to a single backend

So we optimize space

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

backend/python/transformers/backend.py

@@ -22,6 +22,8 @@ import torch.cuda
 
 XPU=os.environ.get("XPU", "0") == "1"
 from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer, StoppingCriteriaList, StopStringCriteria
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+from scipy.io import wavfile
 
 
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
@@ -191,6 +193,9 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                                                                 export=True,
                                                                 device=device_map)
                 self.OV = True
+            elif request.Type == "MusicgenForConditionalGeneration":
+                self.processor = AutoProcessor.from_pretrained(model_name)
+                self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)                
             else:
                 print("Automodel", file=sys.stderr)
                 self.model = AutoModel.from_pretrained(model_name, 
@@ -380,6 +385,93 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         finally:
             await iterations.aclose()
 
+    def SoundGeneration(self, request, context):
+        model_name = request.model
+        try:
+            if self.processor is None:
+                if model_name == "":
+                    return backend_pb2.Result(success=False, message="request.model is required")
+                self.processor = AutoProcessor.from_pretrained(model_name)
+            if self.model is None:
+                if model_name == "":
+                    return backend_pb2.Result(success=False, message="request.model is required")
+                self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
+            inputs = None
+            if request.text == "":
+                inputs = self.model.get_unconditional_inputs(num_samples=1)
+            elif request.HasField('src'):
+                # TODO SECURITY CODE GOES HERE LOL
+                # WHO KNOWS IF THIS WORKS???
+                sample_rate, wsamples = wavfile.read('path_to_your_file.wav')
+                
+                if request.HasField('src_divisor'):
+                    wsamples = wsamples[: len(wsamples) // request.src_divisor]
+                
+                inputs = self.processor(
+                    audio=wsamples,
+                    sampling_rate=sample_rate,
+                    text=[request.text],
+                    padding=True,
+                    return_tensors="pt",
+                )
+            else:
+                inputs = self.processor(
+                    text=[request.text],
+                    padding=True,
+                    return_tensors="pt",
+                )
+            
+            tokens = 256
+            if request.HasField('duration'):
+                tokens = int(request.duration * 51.2) # 256 tokens = 5 seconds, therefore 51.2 tokens is one second
+            guidance = 3.0
+            if request.HasField('temperature'):
+                guidance = request.temperature
+            dosample = True
+            if request.HasField('sample'):
+                dosample = request.sample
+            audio_values = self.model.generate(**inputs, do_sample=dosample, guidance_scale=guidance, max_new_tokens=tokens)
+            print("[transformers-musicgen] SoundGeneration generated!", file=sys.stderr)
+            sampling_rate = self.model.config.audio_encoder.sampling_rate
+            wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
+            print("[transformers-musicgen] SoundGeneration saved to", request.dst, file=sys.stderr)
+            print("[transformers-musicgen] SoundGeneration for", file=sys.stderr)
+            print("[transformers-musicgen] SoundGeneration requested tokens", tokens, file=sys.stderr)
+            print(request, file=sys.stderr)
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
+
+# The TTS endpoint is older, and provides fewer features, but exists for compatibility reasons
+    def TTS(self, request, context):
+        model_name = request.model
+        try:
+            if self.processor is None:
+                if model_name == "":
+                    return backend_pb2.Result(success=False, message="request.model is required")
+                self.processor = AutoProcessor.from_pretrained(model_name)
+            if self.model is None:
+                if model_name == "":
+                    return backend_pb2.Result(success=False, message="request.model is required")
+                self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
+            inputs = self.processor(
+                text=[request.text],
+                padding=True,
+                return_tensors="pt",
+            )
+            tokens = 512 # No good place to set the "length" in TTS, so use 10s as a sane default
+            audio_values = self.model.generate(**inputs, max_new_tokens=tokens)
+            print("[transformers-musicgen] TTS generated!", file=sys.stderr)
+            sampling_rate = self.model.config.audio_encoder.sampling_rate
+            wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
+            print("[transformers-musicgen] TTS saved to", request.dst, file=sys.stderr)
+            print("[transformers-musicgen] TTS for", file=sys.stderr)
+            print(request, file=sys.stderr)
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
 async def serve(address):
     # Start asyncio gRPC server
     server = grpc.aio.server(migration_thread_pool=futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))